{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOUvgVp3xnNW"
      },
      "source": [
        "# Target Ensemble\n",
        "\n",
        "Apart from the main target, there are actually many auxilliary targets in the dataset.\n",
        "\n",
        "These targets are fundamentally related to the main target which make them potentially helpful to model. And because these targets have a wide range of correlations to the main targets, it means that we could potentially build some nice ensembles to boost our performance.\n",
        "\n",
        "In this notebook, we will\n",
        "1. Explore the auxilliary targets\n",
        "2. Select our favorite targets to include in the ensemble\n",
        "3. Create an ensemble of models trained on different targets\n",
        "4. Pickle and upload our ensemble model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ej4poji3G1Df",
        "outputId": "171d8edf-bd43-406a-e782-64ed09624904"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Python 3.11.13\n"
          ]
        }
      ],
      "source": [
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KD826S8uxnNY",
        "outputId": "2d93c23b-7937-4dba-8dde-1f51df9884c0"
      },
      "outputs": [],
      "source": [
        "# Install dependencies\n",
        "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n",
        "!pip install -q --no-deps numerai-tools\n",
        "\n",
        "# Inline plots\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwChLrKexnNa"
      },
      "source": [
        "## 1. Auxilliary Targets\n",
        "\n",
        "Let's start by taking a look at the different targets in the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "R1I_xkY4xnNa",
        "outputId": "62fdbb5e-df86-4e4e-f648-fe52d726c949"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v5.1/train.parquet: 2.37GB [04:12, 9.39MB/s]                            \n",
            "v5.1/features.json: 291kB [00:00, 1.77MB/s]                           \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>era</th>\n",
              "      <th>target_agnes_20</th>\n",
              "      <th>target_agnes_60</th>\n",
              "      <th>target_alpha_20</th>\n",
              "      <th>target_alpha_60</th>\n",
              "      <th>target_bravo_20</th>\n",
              "      <th>target_bravo_60</th>\n",
              "      <th>target_caroline_20</th>\n",
              "      <th>target_caroline_60</th>\n",
              "      <th>target_charlie_20</th>\n",
              "      <th>...</th>\n",
              "      <th>target_teager2b_60</th>\n",
              "      <th>target_tyler_20</th>\n",
              "      <th>target_tyler_60</th>\n",
              "      <th>target_victor_20</th>\n",
              "      <th>target_victor_60</th>\n",
              "      <th>target_waldo_20</th>\n",
              "      <th>target_waldo_60</th>\n",
              "      <th>target_xerxes_20</th>\n",
              "      <th>target_xerxes_60</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n0007b5abb0c3a25</th>\n",
              "      <td>0001</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>...</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n003bba8a98662e4</th>\n",
              "      <td>0001</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>...</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n003bee128c2fcfc</th>\n",
              "      <td>0001</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0048ac83aff7194</th>\n",
              "      <td>0001</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>...</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0055a2401ba6480</th>\n",
              "      <td>0001</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>...</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffc2d5e4b79a7ae</th>\n",
              "      <td>0573</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffc9844c1c7a6a9</th>\n",
              "      <td>0573</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffd79773f4109bb</th>\n",
              "      <td>0573</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nfff6ab9d6dc0b32</th>\n",
              "      <td>0573</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>...</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nfff87b21e4db902</th>\n",
              "      <td>0573</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>688184 rows × 38 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-86f786a6-a0b5-49cf-a9e8-33b08d4f2a11');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-e77cb780-76c2-488c-afef-011dd93b07a5\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e77cb780-76c2-488c-afef-011dd93b07a5')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-e77cb780-76c2-488c-afef-011dd93b07a5 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   era  target_agnes_20  target_agnes_60  target_alpha_20  \\\n",
              "id                                                                          \n",
              "n0007b5abb0c3a25  0001             0.25             0.00             0.25   \n",
              "n003bba8a98662e4  0001             0.25             0.25             0.25   \n",
              "n003bee128c2fcfc  0001             1.00             1.00             1.00   \n",
              "n0048ac83aff7194  0001             0.25             0.25             0.25   \n",
              "n0055a2401ba6480  0001             0.25             0.50             0.25   \n",
              "...                ...              ...              ...              ...   \n",
              "nffc2d5e4b79a7ae  0573             0.00             0.25             0.00   \n",
              "nffc9844c1c7a6a9  0573             0.50             0.50             0.25   \n",
              "nffd79773f4109bb  0573             0.50             0.50             0.75   \n",
              "nfff6ab9d6dc0b32  0573             0.50             0.50             0.25   \n",
              "nfff87b21e4db902  0573             0.75             0.75             0.50   \n",
              "\n",
              "                  target_alpha_60  target_bravo_20  target_bravo_60  \\\n",
              "id                                                                    \n",
              "n0007b5abb0c3a25             0.25             0.00             0.00   \n",
              "n003bba8a98662e4             0.00             0.25             0.00   \n",
              "n003bee128c2fcfc             1.00             0.75             1.00   \n",
              "n0048ac83aff7194             0.25             0.50             0.25   \n",
              "n0055a2401ba6480             0.50             0.25             0.50   \n",
              "...                           ...              ...              ...   \n",
              "nffc2d5e4b79a7ae             0.25             0.00             0.25   \n",
              "nffc9844c1c7a6a9             0.50             0.50             0.50   \n",
              "nffd79773f4109bb             0.50             0.75             0.50   \n",
              "nfff6ab9d6dc0b32             0.50             0.50             0.50   \n",
              "nfff87b21e4db902             0.75             0.50             0.75   \n",
              "\n",
              "                  target_caroline_20  target_caroline_60  target_charlie_20  \\\n",
              "id                                                                            \n",
              "n0007b5abb0c3a25                0.25                0.00               0.25   \n",
              "n003bba8a98662e4                0.25                0.25               0.25   \n",
              "n003bee128c2fcfc                0.75                0.75               0.75   \n",
              "n0048ac83aff7194                0.50                0.25               0.50   \n",
              "n0055a2401ba6480                0.25                0.50               0.25   \n",
              "...                              ...                 ...                ...   \n",
              "nffc2d5e4b79a7ae                0.25                0.50               0.00   \n",
              "nffc9844c1c7a6a9                0.50                0.50               0.50   \n",
              "nffd79773f4109bb                0.50                0.50               0.75   \n",
              "nfff6ab9d6dc0b32                0.25                0.50               0.25   \n",
              "nfff87b21e4db902                0.50                0.50               0.50   \n",
              "\n",
              "                  ...  target_teager2b_60  target_tyler_20  target_tyler_60  \\\n",
              "id                ...                                                         \n",
              "n0007b5abb0c3a25  ...                0.50             0.25             0.25   \n",
              "n003bba8a98662e4  ...                0.50             0.25             0.25   \n",
              "n003bee128c2fcfc  ...                1.00             1.00             0.75   \n",
              "n0048ac83aff7194  ...                0.25             0.25             0.25   \n",
              "n0055a2401ba6480  ...                0.50             0.25             0.50   \n",
              "...               ...                 ...              ...              ...   \n",
              "nffc2d5e4b79a7ae  ...                0.50             0.25             0.50   \n",
              "nffc9844c1c7a6a9  ...                0.75             0.50             0.50   \n",
              "nffd79773f4109bb  ...                0.75             0.50             0.50   \n",
              "nfff6ab9d6dc0b32  ...                0.50             0.50             0.25   \n",
              "nfff87b21e4db902  ...                0.75             0.75             0.75   \n",
              "\n",
              "                  target_victor_20  target_victor_60  target_waldo_20  \\\n",
              "id                                                                      \n",
              "n0007b5abb0c3a25              0.25              0.25             0.25   \n",
              "n003bba8a98662e4              0.25              0.00             0.25   \n",
              "n003bee128c2fcfc              0.75              0.75             0.75   \n",
              "n0048ac83aff7194              0.50              0.25             0.25   \n",
              "n0055a2401ba6480              0.25              0.50             0.25   \n",
              "...                            ...               ...              ...   \n",
              "nffc2d5e4b79a7ae              0.25              0.50             0.00   \n",
              "nffc9844c1c7a6a9              0.50              0.50             0.50   \n",
              "nffd79773f4109bb              0.50              0.50             0.50   \n",
              "nfff6ab9d6dc0b32              0.25              0.50             0.50   \n",
              "nfff87b21e4db902              0.50              0.75             0.50   \n",
              "\n",
              "                  target_waldo_60  target_xerxes_20  target_xerxes_60  target  \n",
              "id                                                                             \n",
              "n0007b5abb0c3a25             0.00              0.25              0.00    0.25  \n",
              "n003bba8a98662e4             0.25              0.25              0.25    0.25  \n",
              "n003bee128c2fcfc             1.00              0.75              0.75    0.75  \n",
              "n0048ac83aff7194             0.25              0.25              0.25    0.25  \n",
              "n0055a2401ba6480             0.50              0.25              0.50    0.25  \n",
              "...                           ...               ...               ...     ...  \n",
              "nffc2d5e4b79a7ae             0.50              0.00              0.25    0.00  \n",
              "nffc9844c1c7a6a9             0.50              0.50              0.50    0.25  \n",
              "nffd79773f4109bb             0.75              0.50              0.50    0.50  \n",
              "nfff6ab9d6dc0b32             0.50              0.25              0.50    0.25  \n",
              "nfff87b21e4db902             0.50              0.50              0.50    0.50  \n",
              "\n",
              "[688184 rows x 38 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "from numerapi import NumerAPI\n",
        "\n",
        "# Set the data version to one of the most recent versions\n",
        "DATA_VERSION = \"v5.1\"\n",
        "MAIN_TARGET = \"target_cyrusd_20\"\n",
        "TARGET_CANDIDATES = [\n",
        "  MAIN_TARGET,\n",
        "  \"target_victor_20\",\n",
        "  \"target_xerxes_20\",\n",
        "  \"target_teager2b_20\"\n",
        "]\n",
        "FAVORITE_MODEL = \"v5_lgbm_ct_blend\"\n",
        "\n",
        "# Download data\n",
        "napi = NumerAPI()\n",
        "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n",
        "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n",
        "\n",
        "# Load data\n",
        "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n",
        "feature_cols = feature_metadata[\"feature_sets\"][\"small\"]\n",
        "# use \"medium\" or \"all\" for better performance. Requires more RAM.\n",
        "# features = feature_metadata[\"feature_sets\"][\"medium\"]\n",
        "# features = feature_metadata[\"feature_sets\"][\"all\"]\n",
        "target_cols = feature_metadata[\"targets\"]\n",
        "train = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/train.parquet\",\n",
        "    columns=[\"era\"] + feature_cols + target_cols\n",
        ")\n",
        "\n",
        "# Downsample to every 4th era to reduce memory usage and speedup model training (suggested for Colab free tier)\n",
        "# Comment out the line below to use all the data (higher memory usage, slower model training, potentially better performance)\n",
        "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n",
        "\n",
        "# Print target columns\n",
        "train[[\"era\"] + target_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YzbRO5uxnNa"
      },
      "source": [
        "### The main target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1o6PJcbxnNa"
      },
      "source": [
        "First thing to note is that `target` is just an alias for the `cyrus` target, so we can drop this column for the rest of the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "pP6LnWcExnNa"
      },
      "outputs": [],
      "source": [
        "# Drop `target` column\n",
        "assert train[\"target\"].equals(train[MAIN_TARGET])\n",
        "targets_df = train[[\"era\"] + target_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d46TQDtrxnNb"
      },
      "source": [
        "### Target names\n",
        "\n",
        "At a high level, each target represents a different kind of stock market return\n",
        "- the `name` represents the type of stock market return (eg. residual to market/country/sector vs market/country/style)\n",
        "- the `_20` or `_60` suffix denotes the time horizon of the target (ie. 20 vs 60 market days)\n",
        "\n",
        "The reason why `cyrus` as our main target is because it most closely matches the type of returns we want for our hedge fund. Just like how we are always in search for better features to include in the dataset, we are also always in search for better targets to make our main target. During our research, we often come up with targets we like but not as much as the main target, and these are instead released as auxilliary targets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "P7uAdarxxnNb",
        "outputId": "ece63dd6-310d-4b40-c863-c22be9170fad"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"agnes\",\n          \"alpha\",\n          \"echo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"20\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"target_agnes_20\",\n          \"target_alpha_20\",\n          \"target_echo_20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"60\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"target_agnes_60\",\n          \"target_alpha_60\",\n          \"target_echo_60\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-8c17913b-801b-4550-ad3c-c81e6b8f590b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>20</th>\n",
              "      <th>60</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>agnes</th>\n",
              "      <td>target_agnes_20</td>\n",
              "      <td>target_agnes_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alpha</th>\n",
              "      <td>target_alpha_20</td>\n",
              "      <td>target_alpha_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bravo</th>\n",
              "      <td>target_bravo_20</td>\n",
              "      <td>target_bravo_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caroline</th>\n",
              "      <td>target_caroline_20</td>\n",
              "      <td>target_caroline_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charlie</th>\n",
              "      <td>target_charlie_20</td>\n",
              "      <td>target_charlie_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>claudia</th>\n",
              "      <td>target_claudia_20</td>\n",
              "      <td>target_claudia_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrusd</th>\n",
              "      <td>target_cyrusd_20</td>\n",
              "      <td>target_cyrusd_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delta</th>\n",
              "      <td>target_delta_20</td>\n",
              "      <td>target_delta_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>echo</th>\n",
              "      <td>target_echo_20</td>\n",
              "      <td>target_echo_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jeremy</th>\n",
              "      <td>target_jeremy_20</td>\n",
              "      <td>target_jeremy_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ralph</th>\n",
              "      <td>target_ralph_20</td>\n",
              "      <td>target_ralph_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rowan</th>\n",
              "      <td>target_rowan_20</td>\n",
              "      <td>target_rowan_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sam</th>\n",
              "      <td>target_sam_20</td>\n",
              "      <td>target_sam_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>teager2b</th>\n",
              "      <td>target_teager2b_20</td>\n",
              "      <td>target_teager2b_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tyler</th>\n",
              "      <td>target_tyler_20</td>\n",
              "      <td>target_tyler_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>victor</th>\n",
              "      <td>target_victor_20</td>\n",
              "      <td>target_victor_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>waldo</th>\n",
              "      <td>target_waldo_20</td>\n",
              "      <td>target_waldo_60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xerxes</th>\n",
              "      <td>target_xerxes_20</td>\n",
              "      <td>target_xerxes_60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c17913b-801b-4550-ad3c-c81e6b8f590b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8c17913b-801b-4550-ad3c-c81e6b8f590b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c17913b-801b-4550-ad3c-c81e6b8f590b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-9ff1255c-0697-43f1-ae0c-6252eec64133\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ff1255c-0697-43f1-ae0c-6252eec64133')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-9ff1255c-0697-43f1-ae0c-6252eec64133 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                          20                  60\n",
              "name                                            \n",
              "agnes        target_agnes_20     target_agnes_60\n",
              "alpha        target_alpha_20     target_alpha_60\n",
              "bravo        target_bravo_20     target_bravo_60\n",
              "caroline  target_caroline_20  target_caroline_60\n",
              "charlie    target_charlie_20   target_charlie_60\n",
              "claudia    target_claudia_20   target_claudia_60\n",
              "cyrusd      target_cyrusd_20    target_cyrusd_60\n",
              "delta        target_delta_20     target_delta_60\n",
              "echo          target_echo_20      target_echo_60\n",
              "jeremy      target_jeremy_20    target_jeremy_60\n",
              "ralph        target_ralph_20     target_ralph_60\n",
              "rowan        target_rowan_20     target_rowan_60\n",
              "sam            target_sam_20       target_sam_60\n",
              "teager2b  target_teager2b_20  target_teager2b_60\n",
              "tyler        target_tyler_20     target_tyler_60\n",
              "victor      target_victor_20    target_victor_60\n",
              "waldo        target_waldo_20     target_waldo_60\n",
              "xerxes      target_xerxes_20    target_xerxes_60"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Print target names grouped by name and time horizon\n",
        "pd.set_option('display.max_rows', 100)\n",
        "t20s = [t for t in target_cols if t.endswith(\"_20\")]\n",
        "t60s = [t for t in target_cols if t.endswith(\"_60\")]\n",
        "names = [t.replace(\"target_\", \"\").replace(\"_20\", \"\") for t in t20s]\n",
        "pd.DataFrame({\"name\": names,\"20\": t20s,\"60\": t60s}).set_index(\"name\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDkTNQMrxnNb"
      },
      "source": [
        "### Target values\n",
        "\n",
        "Note that some targets are binned into 5 bins while others are binned into 7 bins.\n",
        "\n",
        "Unlike feature values which are integers ranging from 0-4, target values are floats which range from 0-1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "Uw_4oswnxnNb",
        "outputId": "a208b358-fc87-4423-bf2a-9e8d67007274"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: >, <Axes: >],\n",
              "       [<Axes: >, <Axes: >]], dtype=object)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x400 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot target distributions\n",
        "targets_df[TARGET_CANDIDATES].plot(\n",
        "  title=\"Target Distributions\",\n",
        "  kind=\"hist\",\n",
        "  bins=35,\n",
        "  density=True,\n",
        "  figsize=(8, 4),\n",
        "  subplots=True,\n",
        "  layout=(2, 2),\n",
        "  ylabel=\"\",\n",
        "  yticks=[]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DM-mHG_xnNc"
      },
      "source": [
        "It is also important to note that the auxilary targets can be `NaN`, but the primary target will never be `NaN`. Since we are using tree-based models here we won't need to do any special pre-processing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "FT3YCXrYxnNc",
        "outputId": "1dbaa0ec-86b7-43ed-dde8-6be3c37c02e6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipython-input-7-1209343005.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Number of NaNs per Era'}, xlabel='era'>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqQAAAGJCAYAAABcnCHcAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAzSBJREFUeJzsnXd8G/X5xz932svynrGz94QEQiCEMENKgVJKy0jZ0BHKKi0NZdM2FGjLry2j0AKFMktbKFAoAUICTQIkECAh07HjvYdsyZp3vz/uvifJ1jjJsi3Jz/v10ivx6XQ6rbvPPePzcKIoiiAIgiAIgiCIMYIf6x0gCIIgCIIgxjckSAmCIAiCIIgxhQQpQRAEQRAEMaaQICUIgiAIgiDGFBKkBEEQBEEQxJhCgpQgCIIgCIIYU0iQEgRBEARBEGMKCVKCIAiCIAhiTCFBShAEQRAEQYwpJEgJgkhL3n//fXAch5dffnmsd0UVra2t+Na3voWCggJwHIcHH3xwrHeJIAgiYyBBShDjmKeeegocx8FoNKKxsXHI/StXrsS8efPGYM8yjxtuuAH//e9/sW7dOjzzzDM4/fTTo67LcRw4jsNvfvObIfexz2T79u0J78N4+zzZ+xjp9v3vf3+sd48giATQjvUOEAQx9ng8Htx77734wx/+MNa7krG89957OPvss3HTTTepfsz999+PH/zgBzCbzSndl/H0eZ566qm4+OKLhyyfMWPGGOwNQRDJQoKUIAgsWrQIjz/+ONatW4fy8vKx3p1Rxel0wmKxDHs7bW1tyM3NVb3+okWLsHPnTjz66KO48cYbh/38g7edDZ+n2+2GXq8Hz0dP5s2YMQNr1qxJeNsulyvlFwIEQSQPpewJgsAtt9yCQCCAe++9N+Z6tbW14DgOTz311JD7OI7DnXfeqfx95513guM47N+/H2vWrIHdbkdRURFuu+02iKKI+vp6nH322cjJyUFpaWnE9DUABAIB3HLLLSgtLYXFYsFZZ52F+vr6Iet99NFHOP3002G322E2m3HCCSfgf//7X9g6bJ+++uorXHjhhcjLy8Py5ctjvuZDhw7hvPPOQ35+PsxmM4455hi88cYbyv0sTS6KIh566CElZRyP4447DieddBLuu+8+DAwMxFz3iy++wKWXXoopU6bAaDSitLQUl19+OTo7OyOur/bzBIANGzZg+fLlyM3NhdVqxcyZM3HLLbfEfRzHcbjmmmvw7LPPYubMmTAajVi8eDE2b948ZN3GxkZcfvnlKCkpgcFgwNy5c/HEE0+ErcNqhl944QXceuutqKiogNlshsPhiLsv8WClCjt27MCKFStgNpuV1/jqq6/ijDPOQHl5OQwGA6ZOnYp77rkHgUBg2M9LEIR6KEJKEAQmT56Miy++GI8//jh+9rOfpTSq9p3vfAezZ8/GvffeizfeeAO/+MUvkJ+fjz/96U846aST8Otf/xrPPvssbrrpJhx11FFYsWJF2ON/+ctfguM43HzzzWhra8ODDz6IU045BTt37oTJZAIgpctXr16NxYsX44477gDP83jyySdx0kkn4YMPPsDRRx8dts3zzjsP06dPx69+9SuIohh131tbW3HsscfC5XLh2muvRUFBAf7617/irLPOwssvv4xzzjkHK1aswDPPPIPvfve7UdPH0bjzzjuxYsUKPPLIIzGjpBs2bMChQ4dw2WWXobS0FLt378Zjjz2G3bt3Y9u2bUMEsNrPc/fu3fj617+OBQsW4O6774bBYMDBgweHCPlobNq0CS+++CKuvfZaGAwGPPzwwzj99NPx8ccfK7Wqra2tOOaYYxQBW1RUhDfffBNXXHEFHA4Hrr/++rBt3nPPPdDr9bjpppvg8Xig1+tj7oPb7UZHR8eQ5Tk5OWGP7ezsxOrVq3H++edjzZo1KCkpASBdUFitVtx4442wWq147733cPvtt8PhcOD+++9X9T4QBJECRIIgxi1PPvmkCED85JNPxOrqalGr1YrXXnutcv8JJ5wgzp07V/m7pqZGBCA++eSTQ7YFQLzjjjuUv++44w4RgHj11Vcry/x+vzhhwgSR4zjx3nvvVZZ3d3eLJpNJvOSSS5RlGzduFAGIFRUVosPhUJa/9NJLIgDx//7v/0RRFEVBEMTp06eLq1atEgVBUNZzuVzi5MmTxVNPPXXIPl1wwQWq3p/rr79eBCB+8MEHyrK+vj5x8uTJ4qRJk8RAIBD2+teuXatqu6HrnnjiiWJpaanocrlEUQz/TEJfy2Cef/55EYC4efNmZVmin+fvfvc7EYDY3t6uar8HvwYA4vbt25Vlhw8fFo1Go3jOOecoy6644gqxrKxM7OjoCHv8+eefL9rtduW1sc97ypQpEV9vrH2IdHv++efDXjcA8dFHHx2yjUjP9b3vfU80m82i2+1WtR8EQQwfStkTBAEAmDJlCr773e/iscceQ3Nzc8q2e+WVVyr/12g0WLJkCURRxBVXXKEsz83NxcyZM3Ho0KEhj7/44oths9mUv7/1rW+hrKwM//nPfwAAO3fuxIEDB3DhhReis7MTHR0d6OjogNPpxMknn4zNmzdDEISwbartwP7Pf/6Do48+Oiytb7VacfXVV6O2thZfffWVujchBnfeeSdaWlrw6KOPRl2HRYKBYETwmGOOAQB8+umnER+j5vNkNa+vvvrqkPdIDcuWLcPixYuVv6uqqnD22Wfjv//9LwKBAERRxD/+8Q+ceeaZEEVR+Ww6OjqwatUq9Pb2Dtn/Sy65JOz1xuPss8/Ghg0bhtxOPPHEsPUMBgMuu+yyIY8Pfa6+vj50dHTg+OOPh8vlwt69e1XvB0EQw4MEKUEQCrfeeiv8fr+q2kO1VFVVhf1tt9thNBpRWFg4ZHl3d/eQx0+fPj3sb47jMG3aNNTW1gIADhw4AEASMkVFRWG3P//5z/B4POjt7Q3bxuTJk1Xt++HDhzFz5swhy2fPnq3cP1xWrFiBE088MWYtaVdXF6677jqUlJTAZDKhqKhIeQ2DX1so8T7P73znOzjuuONw5ZVXoqSkBOeffz5eeukl1eJ08GcDSE1GLpcL7e3taG9vR09PDx577LEhnw0Th21tbWGPV/vZMCZMmIBTTjllyI2l5BkVFRUR0/+7d+/GOeecA7vdjpycHBQVFSlNUrHeW4IgUgvVkBIEoTBlyhSsWbMGjz32GH72s58NuT9as06sBhCNRqNqGYCY9ZzRYOLp/vvvx6JFiyKuY7Vaw/5OJAI3Gtxxxx1YuXIl/vSnP0Xs1P/2t7+NLVu24Cc/+QkWLVoEq9UKQRBw+umnxxSP8T5Pk8mEzZs3Y+PGjXjjjTfw1ltv4cUXX8RJJ52Et99+O+rnpBa2b2vWrMEll1wScZ0FCxYM2aeRINJ2e3p6cMIJJyAnJwd33303pk6dCqPRiE8//RQ333xzUlFjgiCSgwQpQRBh3Hrrrfjb3/6GX//610Puy8vLAyCdyENJRaQwGiwCyhBFEQcPHlSEzNSpUwFITSynnHJKSp974sSJ2Ldv35DlLJU7ceLElDzPCSecgJUrV+LXv/41br/99rD7uru78e677+Kuu+4Ku2/w+xKNWJ8nAPA8j5NPPhknn3wyfvvb3+JXv/oVfv7zn2Pjxo1x389I+7B//36YzWYUFRUBAGw2GwKBQMo/m1Tw/vvvo7OzE//85z/DmulqamrGcK8IYnxCKXuCIMKYOnUq1qxZgz/96U9oaWkJuy8nJweFhYVDrH0efvjhEdufp59+Gn19fcrfL7/8Mpqbm7F69WoAwOLFizF16lQ88MAD6O/vH/L49vb2pJ/7a1/7Gj7++GNs3bpVWeZ0OvHYY49h0qRJmDNnTtLbHgyrJX3sscfClrMo5eDosdrRpLE+z66uriHrsyizx+OJu+2tW7eG1YDW19fj1VdfxWmnnQaNRgONRoNzzz0X//jHP7Br164hjx/OZ5MKIr23Xq93RL/PBEFEhiKkBEEM4ec//zmeeeYZ7Nu3D3Pnzg2778orr8S9996LK6+8EkuWLMHmzZuxf//+EduX/Px8LF++HJdddhlaW1vx4IMPYtq0abjqqqsASBG+P//5z1i9ejXmzp2Lyy67DBUVFWhsbMTGjRuRk5OD1157Lann/tnPfobnn38eq1evxrXXXov8/Hz89a9/RU1NDf7xj3/ENGxPlBNOOAEnnHACNm3aFLY8JycHK1aswH333Qefz4eKigq8/fbbCUXxon2ed999NzZv3owzzjgDEydORFtbGx5++GFMmDAhrj8rAMybNw+rVq0Ks30CgLvuuktZ595778XGjRuxdOlSXHXVVZgzZw66urrw6aef4p133okoihNh//79+Nvf/jZkeUlJCU499dSYjz322GORl5eHSy65BNdeey04jsMzzzyTVOkIQRDDgwQpQRBDmDZtGtasWYO//vWvQ+67/fbb0d7ejpdffhkvvfQSVq9ejTfffBPFxcUjsi+33HILvvjiC6xfvx59fX04+eST8fDDD4dN2Vm5ciW2bt2Ke+65B3/84x/R39+P0tJSLF26FN/73veSfu6SkhJs2bIFN998M/7whz/A7XZjwYIFeO2113DGGWek4uWFceeddw7pDgeA5557Dj/60Y/w0EMPQRRFnHbaaXjzzTdV+8VG+zzPOuss1NbW4oknnkBHRwcKCwtxwgkn4K677oLdbo+73RNOOAHLli3DXXfdhbq6OsyZMwdPPfVUWF1oSUkJPv74Y9x999345z//iYcffhgFBQWYO3du1DKCRGBd9ZH2LZ4gLSgowOuvv44f//jHuPXWW5GXl4c1a9bg5JNPxqpVq4a9bwRBqIcT6VKQIAiCSBCO47B27Vr88Y9/HOtdIQgiC6AaUoIgCIIgCGJMIUFKEARBEARBjCkkSAmCIAiCIIgxhZqaCIIgiISh9gOCIFJJQhHS9evX46ijjoLNZkNxcTG+8Y1vDDGNdrvdWLt2LQoKCmC1WnHuueeitbU1bJ26ujqcccYZMJvNKC4uxk9+8hP4/f7hvxqCIAiCIAgi40hIkG7atAlr167Ftm3bsGHDBvh8Ppx22mlwOp3KOjfccANee+01/P3vf8emTZvQ1NSEb37zm8r9gUAAZ5xxBrxeL7Zs2YK//vWveOqpp4ZMJyEIgiAIgiDGB8OyfWpvb0dxcTE2bdqEFStWoLe3F0VFRXjuuefwrW99C4A0Ym/27NnYunUrjjnmGLz55pv4+te/jqamJpSUlAAAHn30Udx8881ob2+HXq+P+7yCIKCpqQk2my3qbG2CIAiCIAhi7BBFEX19fSgvL487SGRYNaS9vb0ApEkqALBjxw74fL6wmcWzZs1CVVWVIki3bt2K+fPnK2IUAFatWoUf/OAH2L17N4444oghz+PxeMLG2DU2NqZ0ZB9BEARBEAQxMtTX12PChAkx10lakAqCgOuvvx7HHXcc5s2bBwBoaWmBXq9Hbm5u2LolJSXKDOWWlpYwMcruZ/dFYv369WGj6Bj19fXIyclJ9iUQBEEQBEEQI4TD4UBlZSVsNlvcdZMWpGvXrsWuXbvw4YcfJrsJ1axbtw433nij8jd7gTk5OSRICYIgCIIg0hg15ZVJCdJrrrkGr7/+OjZv3hwWgi0tLYXX60VPT09YlLS1tRWlpaXKOh9//HHY9lgXPltnMAaDAQaDIZldJQiCIAiCINKchLrsRVHENddcg3/961947733MHny5LD7Fy9eDJ1Oh3fffVdZtm/fPtTV1WHZsmUAgGXLluHLL79EW1ubss6GDRuQk5NDdaEEQRAEQRDjkIQipGvXrsVzzz2HV199FTabTan5tNvtMJlMsNvtuOKKK3DjjTciPz8fOTk5+NGPfoRly5bhmGOOAQCcdtppmDNnDr773e/ivvvuQ0tLC2699VasXbuWoqAEQRAEQRDjkIRsn6LVADz55JO49NJLAUjG+D/+8Y/x/PPPw+PxYNWqVXj44YfD0vGHDx/GD37wA7z//vuwWCy45JJLcO+990KrVaePHQ4H7HY7ent7qYaUIAiCIAgiDUlErw3Lh3SsIEFKEARBEASR3iSi1xKqISUIgiAIgiCIVEOClCAIgiAIghhTSJASBEEQBEEQYwoJUoIgCIIgCGJMIUGa5QREER/39MMjCGO9KwRBEARBEBEhQZrlvNjchbM+O4jf1LSM9a4QBEEQBEFEhARplrOxqw8AcGjAM8Z7QhAEQRAEERkSpFnOdocTANDnp5Q9QRAEQRDpCQnSLKbR7UWzxwcAcPgDY7w3BEEQBEEQkSFBmsWw6ChAgpQgCIIgiPSFBGkWs703RJAGSJASBEEQBJGekCDNYrb3upT/91GElCAIgiCINIUEaZYyEBDwZX9QkLoFkbxICYIgCIJIS0iQZilf9LngF4EivVZZRnWkBEEQBEGkIyRIs5TtDik6erTdAqtG+pjJ+okgCIIgiHSEBGmWwhqaFudYkKPVAKAIKUEQBEEQ6QkJ0ixEFEXF8ukouwU2EqQEQRAEQaQxJEizkDq3F+1eP3Qch/lWE+wkSAmCIAiCSGNIkGYhLF0/32aCUcPDppEFKXmREgRBEASRhpAgzUJYQ9OSHAsAIEfLmppIkBIEQRAEkX6QIM1CdsgR0iV2SZCyGtJeEqQEQRAEQaQhJEizDGcggN3OAQDAkhwzACg1pBQhJQiCIAgiHSFBmmXsdLgQEIFygw7lRj0AhNg+kQ8pQRAEQRDpBwnSLOOgywMAmGc1KcvI9okgCIIgiHSGBGmW4RVEAIBJE/xoyfaJIAiCIIh0JmFBunnzZpx55pkoLy8Hx3F45ZVXwu7nOC7i7f7771fWmTRp0pD777333mG/GALwi5Ig1XGcssxGNaQEQRAEQaQxCQtSp9OJhQsX4qGHHop4f3Nzc9jtiSeeAMdxOPfcc8PWu/vuu8PW+9GPfpTcKyDCYIJUGyJIc+RoKXXZEwRBEASRjmgTfcDq1auxevXqqPeXlpaG/f3qq6/ixBNPxJQpU8KW22y2IesSw8cXSZDq5AgpGeMTBEEQBJGGjGgNaWtrK9544w1cccUVQ+679957UVBQgCOOOAL3338//H5/1O14PB44HI6wGxEZJULKh0ZIgzWkonw/QRAEQRBEupBwhDQR/vrXv8Jms+Gb3/xm2PJrr70WRx55JPLz87FlyxasW7cOzc3N+O1vfxtxO+vXr8ddd901kruaNfgFVkMaXMZsn/wiMCCIMGu4SA8lCIIgCIIYE0ZUkD7xxBO46KKLYDQaw5bfeOONyv8XLFgAvV6P733ve1i/fj0MBsOQ7axbty7sMQ6HA5WVlSO34xkMS9lrQlL2Fg0PHoAAKUpq1pC5AkEQBEEQ6cOICdIPPvgA+/btw4svvhh33aVLl8Lv96O2thYzZ84ccr/BYIgoVImhROqy5zgOOVoNevwBOPwBlBp0Y7V7BEEQBEEQQxixUNlf/vIXLF68GAsXLoy77s6dO8HzPIqLi0dqd8YNfrlENLSpCSDrJ4IgCIIg0peEI6T9/f04ePCg8ndNTQ127tyJ/Px8VFVVAZBS6n//+9/xm9/8Zsjjt27dio8++ggnnngibDYbtm7dihtuuAFr1qxBXl7eMF4KAYRESPlwQZqjJesngiAIgiDSk4QF6fbt23HiiScqf7PazksuuQRPPfUUAOCFF16AKIq44IILhjzeYDDghRdewJ133gmPx4PJkyfjhhtuCKsRJZLHJwy1fQJC59mTICUIgiAIIr1IWJCuXLkyrnXQ1VdfjauvvjrifUceeSS2bduW6NMSKolkjA8EBSl5kRIEQRAEkW5Qu3WWEampCQBsihepMOr7RBAEQRAEEQsSpFlG0PYpfDml7AmCIAiCSFdIkGYZ0Zqa7CRICYIgCIJIU0iQZhksI0+2TwRBEARBZAokSLOMaDWkLGVPtk8EQRAEQaQbJEizDF+8LnsSpARBEARBpBkkSLOMQFRBKn3UVENKEARBEES6QYI0y/BFm9TEbJ/Ih5QgCIIgiDSDBGmW4ReY7dMgQaqjLnuCIAiCINITEqRZRtSmJg2rIRUgxJm0RRAEQRAEMZqQIM0ygk1N4cuZ7ZMIwBmgaU0EQRAEQaQPJEizjGgRUiPPKcvI+okgCIIgiHSCBGmWEc32ieM4sn4iCIIgCCItIUGaZQTk8lDtoC57gKyfCIIgCIJIT0iQZhk+IXLKHgjWkVLKniAIgiCIdIIEaZbhj5KyBwA7pewJgiAIgkhDSJBmGbEEKashdVCXPUEQBEEQaQQJ0ixDEaQRakhtGoqQEgRBEASRfpAgzTJ8UWyfgGDKnmpICYIgCIJIJ0iQZhGiKCpd9pqhelRpaqIIKUEQBEEQ6QQJ0izCHzIRNFKElGyfCIIgCIJIR0iQZhG+kBn1ZPtEEARBEESmQII0i/CHCNJITU1k+0QQBEEQRDpCgjSLCBOksWyf/GT7RBAEQRBE+kCCNIvwy1OaOACaGCn7vgBFSAmCIAiCSB9IkGYRsSyfALJ9IgiCIAgiPUlYkG7evBlnnnkmysvLwXEcXnnllbD7L730UnAcF3Y7/fTTw9bp6urCRRddhJycHOTm5uKKK65Af3//sF4IEUzZR4qOAkFjfFdAUKKpBEEQBEEQY03CgtTpdGLhwoV46KGHoq5z+umno7m5Wbk9//zzYfdfdNFF2L17NzZs2IDXX38dmzdvxtVXX5343hNhMEGqi/KpshpSgNL2BEEQBEGkD9pEH7B69WqsXr065joGgwGlpaUR79uzZw/eeustfPLJJ1iyZAkA4A9/+AO+9rWv4YEHHkB5eXmiu0TI+GLMsQcAHc/BxHMYEEQ4/AHk6RL++AmCIAiCIFLOiNSQvv/++yguLsbMmTPxgx/8AJ2dncp9W7duRW5uriJGAeCUU04Bz/P46KOPIm7P4/HA4XCE3YihsDR8tBpSILTTniKkBEEQBEGkBykXpKeffjqefvppvPvuu/j1r3+NTZs2YfXq1QjIKeKWlhYUFxeHPUar1SI/Px8tLS0Rt7l+/XrY7XblVllZmerdzgrYpKZoEVKABClBEARBEOlHynO2559/vvL/+fPnY8GCBZg6dSref/99nHzyyUltc926dbjxxhuVvx0OB4nSCPjjpOyB0Hn25EVKEARBEER6MOK2T1OmTEFhYSEOHjwIACgtLUVbW1vYOn6/H11dXVHrTg0GA3JycsJuxFAU26cIU5oYZP1EEARBEES6MeKCtKGhAZ2dnSgrKwMALFu2DD09PdixY4eyznvvvQdBELB06dKR3p2shtWQRrN9AsgcnyAIgiCI9CPhlH1/f78S7QSAmpoa7Ny5E/n5+cjPz8ddd92Fc889F6WlpaiursZPf/pTTJs2DatWrQIAzJ49G6effjquuuoqPProo/D5fLjmmmtw/vnnU4f9MPHHMcYHgBwN1ZASBEEQBJFeJBwh3b59O4444ggcccQRAIAbb7wRRxxxBG6//XZoNBp88cUXOOusszBjxgxcccUVWLx4MT744AMYDAZlG88++yxmzZqFk08+GV/72tewfPlyPPbYY6l7VeOUeLZPAGDTSh85pewJgiAIgkgXEo6Qrly5EqIYfcrPf//737jbyM/Px3PPPZfoUxNxUBMhNfKSIPXSpCaCIAiCINIEmmWfRTDbJ010PQqD3PDkFajLniAIgiCI9IAEaRbhV9Flr5MjpB6KkBIEQRAEkSaQIM0ifEL8GlIWIfXFKLsgCIIgCIIYTUiQZhFqjPH1HEvZkyAlCIIgCCI9IEGaRahpamLpfA/VkBIEQRAEkSaQIM0iFNunGDWkBrmGlFL2BEEQBEGkCyRIswg2qSlWhJRS9gRBEARBpBskSLMIlrKPZfuk50mQEgRBEASRXpAgzSLU1JAqglSkGlKCIAiCINIDEqRZhJrRoTpK2RMEQRAEkWaQIM0i2KSm2D6kNDqUIAiCIIj0ggRpFuFX0WUfTNmTICUIgiAIIj0gQZpFqOmyp5Q9QRAEQRDpBgnSLMKnoqkpmLKnpiYic+j2+dHh9Y/1bhAEQRAjhHasd4BIHUHbJ0rZE9mD0x/Aio/3otPrxwn5Nny7NB+nF9ph0tD1NEEQRLZAgjSLUGX7FJKyF0URXIx1CSId2NU/gHY5Orqxqw8bu/pg0/C4YVIpflhVPMZ7RxAEQaQCCjFkEWpGh7IIqYhgVz5BpDNfOd0AgKPtFtw4qQQTjDr0BQT83+HWMd4zgiAIIlWQIM0iWFOTNkbQUxciVqmOlMgE9vQPAACW5Vrx08ll+M+RMwAADn8AIpWeEARBZAUkSLMIVT6kXPAjpzpSIhP4Shaksy1GAIBVqwEgRfldAbqoIgiCyAZIkGYRampItTynfOhk/USkO4IoYo+csp9jNQEATDwHjfwV7yNBiq/6B9Dg9o71bhAEQQwLEqRZhJoaUoA67YnMod7thTMgwMBzmGIyAAA4joNNI0VJHf7AWO7emPNhdx9O/mQfLvi8eqx3hSAIYliQIM0iAipm2QMhgpRqSIk0h6XrZ5iNYRdaVq106OofB4L0hr11uGJXDZyDXmu/P4Ab9tZDhCTcCYIgMhmyfcoifComNQUCLmhEHwANpeyJtOerfildP9tqDFsuRUh9WZ+y7/cH8HxzFwBgIFCLv86fojQm3l3dpAhRN9m4EQSR4VCENIvwq4iQNjQ+C87fDYBS9kT685VTipDOsZjClufIjU19WR4hdYYI7ve6+nDTvnqIoohNXX14uqkzbF03XWASBJHBUIQ0iwgK0ujreD3t0GGC9H86gRFpzp7+8IYmhlWuIe0LjA9Bypq4XmzpQq5Wg9fbewAA3y0vwDOyMHULAk2vIggiY6GjVxbhUxEhFUQfNJCm3niohpRIY5yBAGoGPAAipOyVGtLs/g67ZMFdqNPivhmVAIA/NbSj0ePDRKMed04tV8Sqm37PBEFkMAkL0s2bN+PMM89EeXk5OI7DK6+8otzn8/lw8803Y/78+bBYLCgvL8fFF1+MpqamsG1MmjQJHMeF3e69995hv5jxjmL7FKPLXhR80MEHIFhzShDpyD6nGyKAIr0WRXpd2H027fiKkFo0GlxUXoCfTi5V7ntwdhUsWg2MvHQYdwfo90wQROaScMre6XRi4cKFuPzyy/HNb34z7D6Xy4VPP/0Ut912GxYuXIju7m5cd911OOuss7B9+/awde+++25cddVVyt82my3Jl0AwfHKAJGaEVPBCK0dIqYaUSGeUdP2g+lEAsMqp6fFSQ2qRX+8NE0tQZdQjR6vBslwrAMDI83AGBIqQEgSR0SQsSFevXo3Vq1dHvM9ut2PDhg1hy/74xz/i6KOPRl1dHaqqqpTlNpsNpaWlgzcREY/HA4/Ho/ztcDgS3e1xgRrbJ0H0BQUpRUiJNEaZ0DQoXQ8EI6T9Wd5lzyZRmWVBynEcvlWaH7aOUc6IDJAgJQgigxnxGtLe3l5wHIfc3Nyw5ffeey8KCgpwxBFH4P7774ff74+6jfXr18Nutyu3ysrKEd7rzMSnYlJTaMqefEiJdIZNaJodIULKjPHHS4TUHKNZid1HKXuCIDKZEe2yd7vduPnmm3HBBRcgJydHWX7ttdfiyCOPRH5+PrZs2YJ169ahubkZv/3tbyNuZ926dbjxxhuVvx0OB4nSCPhVTGoKjZB6KGVPpCmiKGKPHCGdEyFCyozx+7K8qckp18haYghSpYaULjAJgshgRkyQ+nw+fPvb34YoinjkkUfC7gsVlwsWLIBer8f3vvc9rF+/HgaDYci2DAZDxOVEOGp8SEXBCy01NRFpTovXh25/ABoOmG6OkLLXsJR9dkdIB6fsI0GClCCIbGBEUvZMjB4+fBgbNmwIi45GYunSpfD7/aitrR2J3Rk3qBGkguiDjmpIiTSHTWiaajLCGEGM2caZMb5FFuCRMMq+T2SMTxBEJpPyCCkTowcOHMDGjRtRUFAQ9zE7d+4Ez/MoLi5O9e6MK9SMDhUEnxIhpS57Il35Kka6HgBsrMt+nDQ1qUrZZ/l7QRBEdpOwIO3v78fBgweVv2tqarBz507k5+ejrKwM3/rWt/Dpp5/i9ddfRyAQQEtLCwAgPz8fer0eW7duxUcffYQTTzwRNpsNW7duxQ033IA1a9YgLy8vda9sHKJmUpMYavtEKT4iTWENTYMnNDGsrMs+yyOkLkF9yt5Fv2eCIDKYhAXp9u3bceKJJyp/s3rQSy65BHfeeSf+/e9/AwAWLVoU9riNGzdi5cqVMBgMeOGFF3DnnXfC4/Fg8uTJuOGGG8LqSonk8MsBz/gpe9ZlTxFSIj1RLJ8sUSKkrKkpEIAoiuBifOczmcE+pJFQUvYUISUIIoNJWJCuXLkSYoxUb6z7AODII4/Etm3bEn1aIg6iKAZtn2J12QvB0aGUsifSEZ8g4qBLtnyKEiFlTU1+UaqdNGmyVZBKEeBYEVKT0tREv2eCIDIXmmWfJYRaEMbsshe95ENKpDU9fj/8IsABKDfoIq5j1vBg3/Js7rR3qWlqoi57giCyABKkWYI/JNoZe3RoiA8pRVSINKRXrgu1aXnwUb7LPMeFjA/NXiGmKmXPsy777H0fCILIfkiQZglqBakoBrvsfZSyJ9KQXp8kSO3a2BVFivVTFkdIlUlNfKwaUprURBBE5kOCNEsIFZfxbJ+YD6mHmiCINKRHjpDmaqOnqQHAOg7GhyZk+0QRUoIgMhgSpFlCaIQ0Vn+HEGL75BPpBEakHyxlb48jSHPkTvv+rE7Zy01N2vgp+wESpARBZDAkSLOEUA/SWBY4oSl7j5C9kSUic+nxSRdMdl1sQcpS9o4sTtm7KGVPEMQ4gQRplqBmSpMoChBFf7CpiVL2RBriUBkhzfaUfUAUFSunWF32JkrZEwSRBZAgzRLUmOKLohQZDdo+ZeeJnMhselQKUluWp+xdIReM1GVPEES2Q4I0S/CrNMUHQLPsibSmV2lqitNlr8nuLnvWYc8DMMT4XVNTE0EQ2QAJ0iyBCVKNiggpzbLPPtq9Phy3bQ9+U9My1rsybBTbpzg1pFY2PjRLU/ahHfax6sKphpQgiGyABGmWoIwNjWP5BECxfSJBmj1s73WiesCDv7d2jfWuDBvVKXs5QtqfpbXQrMM+Vv0oQDWkBEFkByRIswS/wLrsYwlSr7SOUkNKEZVsgaW5m9w+CBleiqG2qUkxxs/SCKliih+jfhQAjBqyfSIIIvMhQZolBG2fEkjZZ7hwIYIwQeoVRXR4/WO8N8Ojxy/tf1xjfJayz9IaUjWm+EBohJR+zwRBZC4kSLMEnwpBOjRCOvL7RYwOvSFRwgaPdwz3ZPiorSFVUvZZ2mWvZo49ENLUFBAg0kUmQRAZCgnSLCHYZR99HWFIhHTEd4sYJRwhgrTR7RvDPRkeAVFEnyzExvsse1ZDaoorSKWLUAGU9SAIInMhQZolqPIhFcJ9SH107soaenyhgjRzI6Shkd74xvisyz47I6RqU/bGkPvdWdrgRRBE9kOCNEtQ19Q02Id05PeLGB3CIqQZnLJnr8Os4WN66gLBCGl/1kZImSCNLcz1HAf2TlEdKUEQmQoJ0ixBje3T4KYmv8hRzVmW0JslKXsW6Y3X0AQANjky6BFEeLKww9ylssue4zgyxycIIuMhQZolqOmyZ01NzIcUoJqzbCFbmpp6VVo+AYA1ZJ1sbGxSm7IHABNZPxEEkeGQIM0SVAlSMbzLHiAv0mwhW5qamOWTGkGq4TglepiNaXu1XfZAaKc9/Z4JgshMSJBmCYrtU4y6O1EIT9kDJEizhZ4QQdrp82MgQ5tb1Fo+MWya7B0fyrrs46XsAZpnTxBE5kOCNEtgTU1qRofyEKERmfUTncAyHZ8gKuld9oNu9mRmlDSRlD0Qav2Ufd9jl6CuqQkIWj+RICUIIlMhQZolJDKpCQjxIqUIacYTmq6fZDIAyFzrJyZIc+N4kDKYOX5WRkj9CaTsNZSyJwgisyFBmiUEBWn0dViEFKB59tkEE3FWDY+JJj2AzG1sSjxCympIsy8yqHaWPUApe4IgMh8SpFkCM7mPVUPKmpqAYKc9ddlnPqEirsIgCdJMbWzqSbSGVJu9EdJgyl6NIKUue4IgMpuEBenmzZtx5plnory8HBzH4ZVXXgm7XxRF3H777SgrK4PJZMIpp5yCAwcOhK3T1dWFiy66CDk5OcjNzcUVV1yB/v7+Yb2Q8Y6aGlJRCApSDaXss4bekM70CqMOQOaa4/cm0GUPANZsTtkn0NTExouSMT5BEJlKwoLU6XRi4cKFeOihhyLef9999+H3v/89Hn30UXz00UewWCxYtWoV3G63ss5FF12E3bt3Y8OGDXj99dexefNmXH311cm/CkKlD2kwaqZTUvYUUcl0WIQ0R6tBhZFFSDNVkFLKnqHWGB8ItX3KvveBIIjxgbrOgRBWr16N1atXR7xPFEU8+OCDuPXWW3H22WcDAJ5++mmUlJTglVdewfnnn489e/bgrbfewieffIIlS5YAAP7whz/ga1/7Gh544AGUl5cP4+WMX9T5kFJTUzbCmprsOg0qDHKENENT9sGmJrW2T9J6jiyMkLpUjg4FABPVkBIEkeGktIa0pqYGLS0tOOWUU5RldrsdS5cuxdatWwEAW7duRW5uriJGAeCUU04Bz/P46KOPIm7X4/HA4XCE3YhwVI0OjdTURDWkGY9Sd6nVYAKLkHq8GTkWNuhDqu5a2arYPmWXIPULopJ+V9dlz2yfMu8zJwiCAFIsSFtaWgAAJSUlYctLSkqU+1paWlBcXBx2v1arRX5+vrLOYNavXw+73a7cKisrU7nbWYFfhTE+i5DyvIlS9lmEIyTNXWrQgYMkTDp9mSXSBFFMPGXPJjUNGh36dkcv6gY8qd3BUcQV8rs085SyJwgi+8mILvt169aht7dXudXX14/1LqUdqnxI2aQmrSWYss/AKBoRTlDEaWHgeRTrpehipjU29QcEMDmVsDF+SMp+W08/Lv6yBud/fgiBDP1+s3S9hgMMMS4yGUyQUpc9QRCZSkoFaWlpKQCgtbU1bHlra6tyX2lpKdra2sLu9/v96OrqUtYZjMFgQE5OTtiNCMcnqPAhlW2fNBoz1ZBmEYOjipna2MReh4HnlK7xeFjZ6NCQlP0nvU4AwKEBD/7b0ZvivRwdWIe9RcODi3GRySDbJ4IgMp2UCtLJkyejtLQU7777rrLM4XDgo48+wrJlywAAy5YtQ09PD3bs2KGs895770EQBCxdujSVuzOu8KuoIRUEJkitISl7EqSZjiOkyx5AxnqR9voSs3wCghHS0JT9l/0Dyv8frW9P0d6NLoopPq/uvaBJTQRBZDoJd9n39/fj4MGDyt81NTXYuXMn8vPzUVVVheuvvx6/+MUvMH36dEyePBm33XYbysvL8Y1vfAMAMHv2bJx++um46qqr8Oijj8Ln8+Gaa67B+eefTx32w8Avn4c0alL2GkvQhzRDU5pEENbUlKtjEVKp0z7TpjX1JFg/CoTOsg9GSL/scyn//7jXiU97nTjSbknRXo4OwQ57dTED6rInCCLTSViQbt++HSeeeKLy94033ggAuOSSS/DUU0/hpz/9KZxOJ66++mr09PRg+fLleOutt2A0GpXHPPvss7jmmmtw8skng+d5nHvuufj973+fgpczflEVIZWbmjRaCzU1ZRGDI6Ss074p0yKkSQhSJWUvR0j7/AHUDEhC/JSCHLzT6cAj9e14PMMEqTNBQcpS9iRICYLIVBIWpCtXroxpJ8NxHO6++27cfffdUdfJz8/Hc889l+hTEzHwqeiyZxFSqiHNLobUkBoyc1qTYvmkVX9YYhHSAUGAXxCxW07Xlxt0uGVKGd7pdOCN9h7UDXhQZTKkfqdHiETm2AOUsicIIvPJiC57Ij6BBCKkWo016ENKgjSjESNYJWV6U1Ouyjn2QDBCCgD9gQB2yYJ0ntWEOVYTTsizQQDw54aOlO7rSONKYGwoEGL7RBFSgiAyFBKkWUKwy15NU5MZOqohzQoGBFGJjtsHNTW1ev3wZJBASSZlr+d5JV3dFxDwZZ8sSG0mAMD3KosAAM82dypNU5mAM4EpTQCl7AmCyHxIkGYJPjU+pCE1pMGUPZ3AMplev/Q5arhgvWG+TgOTLFBaPCNTR7qrz4W7DzahP4UjO5NpagIAq4Z12geUhqYFVjMA4MR8G2ZajHAGBPzyUDM6vJkhShOZYw8Em5rI9okgiEyFBGmWoKTsY01qkiOkWo2FUvZZQmhUkflVchynpO0bRihtv25/Ix6ub8Mf69rir6ySZGyfAMCmlQ5jnT4/9rvcAIIRUo7j8MNKaTLc002dWLRlFy758hDeaO9Ja9P8RLvsqYaUIIhMhwRplsAipJoYHtrBpqYQH9I0PikT8XH4wjvsGeVKY1PqI6QdXj+2OyTz+RdbulIm7JQIaQI1pABgkyOkn/Q64ReBPK1GaewCgG+X5uG3Myux0GaCXwT+2+HAFbtqceuBxpTs90iQeJc91ZASBJHZkCDNEpgvuFrbJ+qyzw6ipblHsrHpvS4H2Lem2ePDpq6+lGyX2VflJhwhldbf2tMPQIqOhk434jgOF5YX4L9LZmLT0bOUutJnmjrQnKZOBImm7KmGlCCITIcEaZagpoZUUIzxzSEpezqBZTKOaIJ0BKc1behwAAhG755r7kzJdoPlB4m50bGU/Se9Uv3oPKsp6rozLUbcNa0Cx9gt8IvAX9K0+z50dKgaWMreLwJ+usgkCCIDIUGaJaixfVKamjTU1JQt9Pojp+zZtKbDbk9Kn88rCNjYJQnSu6ZVAJBS4J0paBZiE6cSTdmzpibW0DPfZo77mO/LdaXPNHXCmcLGrFSReJd98FBOUVKCIDIREqRZgroIKbN9sii2Tx4h/U7GhHoU785BUcUFsijb4XCl9KLjox4n+gMCivRaXFiWjwVWE3yiiH+2dg9ru5H8VNViG7R+rAgp47TCHEwxGdDrD+D5lq6Enm80SDZlD1CnPUEQmQkJ0izBr2ZSU1gNqfR/EqSZTbQI6WyLEYU6LVwBATscrkgPTYq3O3sBSGM5eY7D+WX5AKS0fawJbvFwCYJyUZVwDWmIaDPxPKaa409k4jkOV8u1pI/Xt6ddx30iTU39zgPwedtC6kjT67UQBEGogQRplqBqlj2LkPImJWXvo2hKRsPGbQ6ebsRzHI7PswIANqeo6UgURbwt14+eWpADAPhmSR4MPIc9Tjc+l03pk4HVwob6qaolNEI612qEJsZvIJRvl+YjX6fBYbcXb7b3JvScI43a0aFebxc+/vhMfPrZxcFO+wD9pgmCyDxIkGYJbFJTrJMxa2rieT30nLR+Jk3yIYbiiBIhBYAV+TYAwKbu1AjSAy4PDru90HMcTsiTtp2r0+JrhXYAwPPDaG5S6kdD/FTVYg157fNU1I8yzBoel5QXAgAerU+dn2oqcAnqRod6PC0QRR/c7jpFkFLKniCITIQEaZYQjJBGvl8UAwCkExXP65VaU2pqCuIRBLzT6cio9yRW3SUTjTsdLvSkYGzmhk4pOnpcnhWWkOe7oKwAAPCvtm4MJBmdS7Z+FAhP2c9XUT8aymUVhdBzHLY7XNje60z4uUcKl8qmpkBAKscQBG8wZU8RUoIgMhASpFmCL04NKYuOAgDH6WCQVyNj/CC3HWjEmi8O4anG9LQCikQsIVdu1GO62QABwP9kj87hsKEjWD8ayvI8KyYYdXD4haQ9SZO1fALCU/ZsQpNaig06fLMkDwDwq0PNENLk96C2hpQJUgAwyKtSDSlBEJkICdIsgU0MjFZDyhqaAIDnddDLnzwZ40u0e314oVnqtt45jFrI0SZeZHGFHCUdrnl9t8+Pj+UI4qmDBCnPcTg2V6pX3eNM7r1jKftEG5oAwCqLNi0HzLIYE378DZNKYOJ5bOnpx5NpcDHiF0R45N9lvJR9qCA18tJjyPaJIIhMhARplhDP9ok1NAFShJSl9n1pEhEaa55o6FCixdXyPPRMIFYNKRCsI/1gmHWk73U6IEASfFWmoV3s082SEDzoSs731JHk2FAAmGExwqzhsSLPBgOf+CFtosmA26aWAQB+Ud2MmiRfQ6pwhQjKhCKkcl041ZASBJGJkCDNEth0lqiCVI6QcpwOHMdBz7Ma0tHZv3TGFRDw16ZgZKza5RmWhdFoIYhicNxmFCF3bK4VGg6oGfCibiB5obVRjrAOjo4ymCA94ExOzPf4pRrXZGpIi/Q6fH7sXDyzYEpSzw0Al1YU4rhcKwYEAdfvrRvT1D2b0qTlAH2cBq9wQSr9mN2B9P/uEgRBDIYEaZYQz/ZJlCOkPC9N8FEEKZ278HJLF7p8AUww6sAD6A8IaE/B5KGRps8fUGbKR4uQ2rQaLM6xAAA2dydfR7qzTxI+S+XU/GCmWaSo6QGXJykxl0hTkyB48NHHX8dXe25Wltm0GtV2T5HgOQ6/m1UJi4bHR71O/LmhPeltDZdQU/x4jgMRBSlFSAmCyEBIkGYBgiiCnYKinZRZUxPHyYKUkz563zg/dwmiiD/VS+LjexOKUWmUZsBXDyOaOFr0yCLOxHMxU9XDrSN1+ANKKn5RFFuliUYDdByHAUFAk8cXcZ1YhNo+xcPpqkF//x60tb2Z8PPEospkwB1TywFIDU5jVbqRyNjQcEEqXUSRICUIIhMhQZoFhNaB6qJ12YtBD1KAIqSMdzodqB7wIEfL44KyfEyRp/wcGuM6QjXEqx9lnCDXkX7Y3ZfURKIv5OjoBKMOhfrIXfA6nsMkk/TdOpiEkFNGoOrid9kH/FKkNxBwQhRTK76+W16AFXlWuAVxzBqcEpnSFCpI9Zz0HlLKniCITIQEaRbgD+mUj1ZDqqTsWYSUl0RMAFzajU0cTR6RDdG/W14Iq1ajjJ1MRlSNNtHGhg7mCJsZVg2Pbn8AXybhIPCZPHo0WnSUEawjTVzMOxJI2QcCzpD/p9YRgeM4fKNYsoGqHqOLEiVlr6JBK0yQyuOAKUJKEEQmQoI0C/CHRkjjNTXJNaSGkOjLeLV++rzPha09Tmg54IoKaWLPFLmD/FCElH21y40/HG5NG+N8JaoYx7tTy3M4Th4jmky3PasfjStIZculA0mI+Z4EBKk/TJCm3sx+ohzpPTzgjbPmyKB2bCgQLsiZIKUuewIAXmzuwvtdjrHeDYJQDQnSLMAXoie10SY1CYNS9lyoIB2fJ7CXZN/Rs4rzUC7Xjk6To3yRUva3HmjELw81K36lY43aCCkAHC/XkX6cxDSinSxCmhNbkE4zs8amJFL2PvW2TwF/MCo4MoJUeh31bu+YZA9Yl72qGlIhNGUvCWgyxie+6HPhur11uGpXbdoMeyCIeJAgzQJYhFTDIWpX7pCmJj4YVRuv05o2y9HCM4rsyjJWQ1o74A0rhQiIoiLmWMRwrElExE0NEVmJ0O71odHjAwdgocqUfaJepKIoole2fVJjjB8IBN0C/CMgSMsMOug5Dj5RTKpBa7i4EomQhohzvSgLUhodOu75rzxVrS8goC7B3zxBjBUkSLOAeJZPACCI4bZPGl4PrZzGH48p+ya3FwdcHvAAlodYGZUbdDDykhgJFW97nW4llZpMHeZIkEjd5QQ5Atzg9ibkscqio9PMhrARnZFgEdJ2rx89PvW2WQdcHgwIIow8h1KDLu76YSl7f+ovDjQcp7gtHB4DtwVXIk1NoRFSSPtKNaTJ4fQHcMeBRvy5oR2dGWD7FosNHcFU/b4kvYEJYrQhQZoFBCOk0QWpkrLnpBMtz+uhxfgVpJvk6OiiHDPsIZ3dPMdhshxNDLV+2h6S6t7rdKdFmUMi3p1MkPYHBOVxamDR4HjRUQCwajUolwXlgQSipNt6pIjnkTkWVZOWAv6RrSEFgKoxrCNNtsteJ0oXSiRIk+Pxhnb8qaEdtx5oxKItu3HZlzV4q70345o+mz1efNEfvGgmQUpkCikXpJMmTQLHcUNua9euBQCsXLlyyH3f//73U70b4wqfoCJCKnfZs6YmjtdBCykKMB5T9ptlT84T5NrKUKYq1k/BA/l2R1D4+EQR+9PgIJ+IIDVpeBTKwjuRtP1Oh3Rii1c/ykimjnSrLEiX5VpUrR8qwkZKkE6SL0rGMkKqrqkpNEIqvedk+5Qc/27rASBlSXyiiDc7enHprhrce6h5bHcsQd7tDG9cJEFKZAopF6SffPIJmpublduGDRsAAOedd56yzlVXXRW2zn333Zfq3RhX+OPMsQcAUfEh1cn/6qFTIqTjK6IiiKIytYjNeg9lqlwLGWr7wyKkJtm/9cv+sU/bJyJIgfC0vRpEUVQipEeoiJACiY8QFUUR2+T3dlmUKVCD8Y9wDSkATJTfq9oxqL8LNjUlJki1FCFNmgNON75yuqHlgHePmomNR83Et0ok+y92wZRuPFzXht/UtAwpwXlbrh9lv1kSpESmkHJBWlRUhNLSUuX2+uuvY+rUqTjhhBOUdcxmc9g6OTmR52MT6lBqSKOY4gNDm5p4Lhgh9Y2zlP0epxudPj/MGh6LI0T+mPUTE6QdXj9q5NTt2bJH5a4k6ki7fH4cdLlV13C2enwxa9nUGuMzJhilz77Bra5Rp8HjQ6fPDy0HzLWaVD1mmiWxxqbDbi+aPT7oOA5H5qiNkIam7EemwWwsI6SJTWoKsX2SBamLBGnCvNbeA0Caapan02K21YS1VcUAJEGXSN31aFA34MHd1U24v7YFb3cG60UHAoJi7fajidL+H3S5M67sgBifjGgNqdfrxd/+9jdcfvnlYd3fzz77LAoLCzFv3jysW7cOLlfsk4rH44HD4Qi7EUF8IV320Rg8qYnj9Yog9YyzgxUbobnMboU+Qs2ikrKXxcincrp+utmg+HkmGiEdCAhYvX0/ln+0Fyd+sg8P17WhLUYHd7fPj5Uf78UZn+6PejJh4zZzVXTZA1AaddSm7FlD02yLCUYV0TpAeo8A9Sn7LXL06Ygcs6oUNTCohtQ/MtEr5kVaNwY1pGpT9qIoDoqQSu8LpewTh6XrzyrOVZZNMRug4aRO9eYxcFuIRagI/WV1s3KM+LC7DwOCiAqDDqsK7TDxHNyCOGaeugSRCCMqSF955RX09PTg0ksvVZZdeOGF+Nvf/oaNGzdi3bp1eOaZZ7BmzZqY21m/fj3sdrtyq6ysHMndzjj8KmpIg01NkVL24+sEptSP5kdOETNB2uTxwRkI4BM5pXyU3YL5NilSuLt/ICF/vycaO3BYFoJ7nW7cXd2EI7buxk/31UfczubuPnT7A6gd8OKrKOI38QhpYil7xRBfZf0oEEzZ1w14w+yH/tzQjrVfHVbS0QzW0HSMXV10FAD8YTWkIxMhrZLfq25/AL0JOAakArVNTYLgBhAypU2QBSlFSBNin9ONvU43dByH0wuDFnAGnleyJemW9n4npIt+v8uNl1okb+QNslA9pSAHGo5Tfo/7nGNfYkQQ8RhRQfqXv/wFq1evRnl5ubLs6quvxqpVqzB//nxcdNFFePrpp/Gvf/0L1dXVUbezbt069Pb2Krf6+vqR3O2Mwy+fk2LVkA5uagpN2Y+nGlJ3QMC23uj1owCQp9MiX4461g54lYamJTkWTDMZYeQ5OAMCalSmc3t8fvz+cCsA4JfTK3D/zAlYkmNGQASebupURnOGwkQzAGzriVwnybw71daQViYqSFWODA2lWK9FjpaHACjvz36nG7cfaMQ/WrvxXFP4UIGtPYnVjwIj70MKABatBkV6qQns8CjXkaqd1DS4oYsEaXK8JkdHT8i3IVcXPvVshoUJuvQRpP3+gJJZWFNWAAC4v6YFroCAd2RBeposrNNx/wkiGiMmSA8fPox33nkHV155Zcz1li5dCgA4ePBg1HUMBgNycnLCbkQQdT6k4Sn7MNuncZSy/6TXCbcgolSvw0w5ehCJ0MgI6zRfbLdAy3OYbZGipGr9SP9Q14ZefwCzLUZcWlGI75YX4vXFM5T0YGj6DZBSsZtCRnxGaqrwCAIG5Mh2woLUE19gCaKIL5KIkHIcp0y7YtZP6w81g0mkxxralYh+g9uLercXGk6KPqtlNGyfgJDGplFOd6pN2YfWjwKAVpC+MyRIEyNSup4xkwm6JKaPjRSbuvvgFUVMNunxi+kVqDDo0OTx4aZ99Wjy+GDieRwnX+DNJEFKZBAjJkiffPJJFBcX44wzzoi53s6dOwEAZWVlI7UrWY9PTZc9i5ByzPZJHxIhHT+ClAm94/OtUadaAcGJTa+19WBAEGDXapT6SJa236WijrTJ7cVfGtoBALdMKQvzij2tQLqw2iB3xTJqBrxhjUfbevuHNFU4QrxE4xnWM1jKvssXgDOOF+mhAQ/6AgJMPBdTuEdCsX5yuvFJrxNvdvSCB5Cj5VHv9uI/8utl6fr5VjOsKl8DMNgYf+QE6Vg1NqkdHTq4XEErSu8n1ZCqZ69zAPtdbug5DqsKhgY60lHQMdP70wrsMGp4/HSydO78Z2s3AKkUidV8p+P+E0Q0RkSQCoKAJ598Epdccgm02mAKpLq6Gvfccw927NiB2tpa/Pvf/8bFF1+MFStWYMGCBSOxK+MCxfYpVpf9YNsnbnwa48fyHw2FRflYCuzIHDN4WUzOs0aOkDa6vXirvTdM7D1Q2wK3IOIYuwWnDDrhnVSQAx7AV053WKMRE81H2y0w8Ry6fIEhEZrgHHs+5kCEUGxajRJNrY8TJWXp+nlWc8zvVSQU6yeXG7+sbgIAnF+WjysnFAEA/lTfBiBYinCMSv9RRmAUakiBsTPHVx8hHSRIA9J31SuK1FWtEhYdXZlvCxuQwWCCbn+adNoLoqgck04tlI4n3yrNwyxL8KLx1IJgHezMENcL/zg6zhOZyYgI0nfeeQd1dXW4/PLLw5br9Xq88847OO200zBr1iz8+Mc/xrnnnovXXnttJHZj3KAmZT+0qUkHnWKMPz5SfJ1ev9Idf3wcQcpS9iz6vCTEkmieHCH9st+lnKR8gojzdlbj0l01mPe/3bjmq8N4sbkLLzRLNZO3TS0fEpHN12mVVPU7IWl7JppPzs/BYvl5B9eRNsoRVLt26Ek0Fmqtn3aw+tEcdXZPoTBB+t8OB7b1OmHkOdw0qRSXVRTCwHPY4XDhk16nUopwbAL1o4LgUTx1gZGrIQVCIqTu0YuQBkQR/bIgtaoUpBqNVFKhCQQj7ZS2j48oikr9aKR0PSAdB7ScNOGsMQ067Xc6XOjw+WHT8Fhql343Go7DLVOCGcbQC99Kox4mnodXFFE7it9jgkiGxM5mKjnttNMiXk1WVlZi06ZNI/GU4xrmIxorUsYipFwE26fxEiH9sKcPIoBZFiNK4sxMZ532jCUhNY6zLSZoOCn13ezxodyox9+aO3FowAMOwIAg4OXWbrwsp9C+VmjH4ig1kqcW5OCjXife7ujFZRWF8AsiPpQjpCvybfCLIj7s6cfWnn5cWlGoPO7lVknoHp+nXswB0glqd787rvUT8zJMpNmIMd0ivXcDsii6ckIRyuVygXNL8vBccxd+Wd2Eavn9OjqRDvtBKfrRqCEdzQjp4QEvfKIIE8+hNM53lAlSva4QA4E6aMU+QD4EuAMiLOqrIMYle51uHHB5YOA5rArprg9Fz/OYYjJiv8uNfU63UvYyVrB68xMLcsJ8p08tyMHtU8th12rCjm08x2GGxYDP+wawt9+tZH4IIh2hWfZZQFIRUk437lL2LMq4XIWIm2QysHM7OEgpe4ZJwytRwF39A3D6A/hNTQsA4FczJuCNI6fj4vIC5Gh52DQ81k2JXh99qnwi/F93P5z+AD7vc6EvICBXq8ECm0kRhNt6gnWkff4AXpcjOxfKXbZqUWP91Oj24qDLAx5QmiMSYaLRoHwX7VoNrpENxgHge5XS/9l0pjlW45DO5lgMFqAjWUM6UY6QNnq8ozY8gll8zbSY4pZiMEGq00vfAR4CdEyQUoQ0Lrvl93pxjiVmHXY61WFu6JSi4KcNKv/hOA4/rCrGReVDjwfptP/J4BNEbOjoxXuDmj+J7GNEIqTE6KJmdKggDrJ9Goc+pDtC/ETjYdLwqDDq0OD2YZbFOOSENc9qwl6nG1/2DeDLvgF0+PyYbNJjTVkBdDyHxXYL7pleAb8gwhLjZDfDbMBEox6H3V5s7u7DHvmkcVyeFRqOwxE5Zug5Dq3ytKgpZgNebevBgCBiutkQcdJULCYY4gvSzXJ0dFGOOWJdXTy0PIfpZgO+crpx7cSSMME502LESfk2vNeVXAR2cN3kSKbsS/RaGGVj8UaPV0nhjyRfyX6Rs63xI1nBCGmesszIc/AFRBKkKmiTp6CVxYlEz7QY8Vr72Au6BrcXu/vd4CHVn6tlpsUEoDutnALiIYoidvUP4KWWLvyztQedshfw+0fPxCxL4mVERGZAEdIsgPmQ6mJ8msyHNNz2afzUkDoDAeyWT/ZLVI6onGqSRMGSCAKWddpv7u7Dw3KTzs+mlIWl0Qw8H1OMAlJkgzUnbOh0DGm6Mml4JTrLutKfa+4EAFxQVhDTKSASlSYVglRl41cs7ptZiVunlOGqCYVD7vtBZTBieow9MUHK5tiz7/FINjVxHIcqoyRCa0ep035PvyQa5qg46So1pForeF76rhrkY4B7nFxkDoc2r3RBzvxmo5EuEUZWZ36U3YL8BC4U02X/E+Fn+xtw6vb9eLyhQxGjAJQhJUR2QoI0CwiODlWfsuf48ZWy3+lwISBK0ZAKlXVgZxbnwsTz+FZJ3pD7WKf9R71OOAMCFtpMOLMoN6l9O03uiv1vh0Mx4T8hxLT/GDmKuKWnH3udA/jU4YKGA84rHbpf8ZgQZ3yoIIrY3B17cIAaltgtuGZiScTRrMvzrDgx34YJRl3CNbAsRa/XS6JWFH0QhJETi5MidNq3enx4tK5N6YZPJSxln0iEVKMxQaORvo9GpYY0+y8yh0u7HCEt1sePkALSRKREprOlmrdlu7TBbh3xYPt/yOUZtdKT4cKs4VYV5uCZ+ZPxQ/kidmeEISJE9kAp+yxAzejQYFNTqO0Ti5BmxkFqOLCucbXRUQBYU16ANRFqsoCgIGXcOqVcsYVKlGNyLbBoeCUSMNGoV+oXASmt/eDhVmzr7Udhs/STPbUgB0VxTqSRYCn7Nq8f7oAwZEb9V/0D6PT5YdbwCZcDqIXjODy3YIry/0RgKXq9vghudwMASZjx/Mik09lMexYhFUURV+2uxce9TvhEET+aWJKy5+r3B5SpULNVRUgl8arRWKDhTfChGwZe+i0PUMo+Lu1yhLQ4ToR0skmqiXYFBDS4vagahdKNwQwEBGU6E8uoqKXCoINZw8MVEHBowKMI1HRlICAoFwu/m1WFfJ1WCrrUB8cZE9kJRUizADU1pMEIaTBlrxtHXfbb5VTPEntqRJZdp1Xmna/Ms+H4YUQT9TyPlSGPHxyZXGI3Q8tJVk1/a5LS9Yk2MzHydRqY5KhlUwQbGxYdPTbXGjG6mSo4jktYjALBpiad1qakqQd33qcSdmFQJwvFtzsd+Fj+LkUb6Zose+WUaqleh4I4IgkIj5DysvWTgZOEKKXs48NqSONd2Ol4TnHdGKu098fyhLkyQ+wJc5HgOQ4zzJmTtm+UPZItGh55cskTG1+81+kekcwEkR6QIM0C1DU1DYqQho4OzfJoiiiKSir8qAQipPFYU16ACUYd7phWPuxtnRZiZr1iUO2mRaPBAvmA3B8QUKzX4qT85MbnchynjBCNlLZPRf3oSMJS9hqtVfHfHJ3xoR4ERBG/rG5W7tvhcCaVwn2jvQe/P9w65LGJpOuBUEFqgUYj15AyQUon7bi0qYyQAmNfh7lJ/l2uyLMldSEX3P/I0+VcAQG/qm7Cj/fWKbef7W9QxgePJqy+fYJRr7zWMoMOxXotAmLQHYHIPihlnwWwGlJdrElNg2tIOd2IpOwDooiBgJDQKMiRpmbAiy5fAAaeU0ztU8G1E0twbYpSticX5EDPceC5yLZUy3Kt+FQuO/h2aX7C05NCmWDUYb/LPaSxyR0QsK1XipAen5+43dNowMSnRmOBVmOFz9c1soJUGR/qxYstXdjvciNXq4FHENDjD6Da5cF0lSlQZyCAn+9vxAstkofsHKsprB7wK1nszLGq+46G15CyCKkfgJZS9nHwCSK6fdK0MzWlL2M90545X5yQZCYmnqD+RXUTnmjsGLL85ZYuvHLENMyzjUz5TiTY0A5WXgRIF9KLbGa83enATodLlVMKkXlQhDQLUJWyF1mXfaQIaWoE6Zd9Lhz/0V4s3LIbrWkw1YTBoqMLrOYRTUMPh0K9Fi8vmoq/L5qGvAgdtKH2SOeX5Q/ruaJ5kX4ipwVL9YmnBUcLVkOq1Vig0ZrlZSM4PlR+r/oDghIdvW5iCRbKJ2j23YrHXucAVm8/oIhRINikwtgjR37mqBS4ASE4qUnDuuw5SWSR7VNsOn1+iAA0nFTGEo+xjJC2e33YpUyYS+5Cke3/9l4XekO61gHgw+4+RYz+sLIY6yaXYd3kMizJMaM/IODCLw6hbpRcJoBg5oY5gjAWyTXtVEeavaTn2ZlICFVNTQLzIQ2tIZVEoycQiPo4NYiiiCca2nHGjgM4NOCBMyAo04YS5ZG6Njwr10mmilTXj44UR+dao175H5drxfF5VlxWUTjsaSvRUvab5M/s+HxrUmnB0SAYITVDo5Heq5E0xzdqeMWnstPnR4VBh8sqCpXJW9tV2NC81d6L1dv3Y7/LjRK9FtfJUfV3Oh3KsANRFENS9iojpP6gIGU1pHr5N+0OUA1pLFi6vlCnVdWMyATdAadn1DvtP5TruudajUk1MgLAUrsFpXodWrw+XPJljVLS0e8P4Ia99QCAi8sLcPu0clw3qQTXTSrBswumYLbFiDavHxd+cQidXn+sp0gZSsp+kD8sqyOlTvtw/tHShf+rHVoClImQIM0CgrZP0deJnbJPPprS6/Pjyt21uOVAI7yiiByt9JVK5iq2xuXBXdVN+PG++pRO5QgK0sxN85g0PP6+aBrWz5gw7G1VRomQKmnBNK0fBUa/hhQI1pECwE8ml8Ko4ZVa5O1xTo6iKOK2g40YEESszLPhnaNm4oaJJTDxPJo8PqUersHjQ19AgJYDppnVdXGHRUjlGlJFkFKENCZtKi2fGJOMBug5DgOCEHfsLsMviGhUuW4s2O/y+GH8Lq1aDZ5bOAU2DY9tvU5cs+cwAqKIu6ubUO/2YoJRh9unhtfC23VaPLdwCioMOhx0eXDxl4dGpaEotIY0FJaVqB7wDInyjlcc/gCu21uH9TXN+Lc8vS+TIUGaBagaHSo3NYUb48sp+2FESH+0pw5vtPdCx3G4e1o5fjldEkw7HYkXnu8JKbj/8b76lBx0+vwBpXs5EcunbCaSF2mXz48v+6T3f3BTVToRmrLXaqxhy0YKVkc602LEeaVSucRiOdq+z+mO+T2tHfCi3u2FjuPwl/mTUKTXwajhsUKu0d0gX3ixdP10s1F1WYlSQ8qbFXGu56TfNNWQxqZdpSk+Q8tzyoWC2rT9ndWNWLz1Kzw3jIyPKIopazScYzXhyfmToec4vN7eizVfHMLT8r49OKsqYt1/mUGP5xdORa5Wgx0OF+6vaR6yTqpRUvaDBGmBPuhs8kUfNTYBwMYuhzIYZ/2h5oxvUCZBmgWwL2SsRhcWIeWUCKl22BFSV0DARvlg+eLCqbi6shhHyHU+u/pdSimBWkIP9M0eH24/2JTUfoXymcMFAdLBrSTOiMDxAjvQt3h9yme0qasPIoBZFiOK0/h9UiKkGktIhHRkU3hrygtwVI4Fv51ZqQyfKNLrFNP8T2NESVkZxBK7GRZN8ITPXBXe7mCCNLGGJiDEh1RrhoaXHqcXpVo/StnHpl2l5VMoidSR9vsDeLZJqhe+9WAjDidZg1k94EGjxwc9x2FpgmN2I7E8z4Y/zKkCByjH7ssqCrE8htidYTHirmkVAKRBICOJTxDRIvcfDBakANWRDmZDRzCTeNjtVWwBMxUSpFmAuqYmFiFlgpSDXl7dk+RV1acOyRy8zKDDslwp+jjFZIBNw2NAEBPuSGUH+tMLc8ABeLGla0jjR6KwppMlI2TynokU6bXQcxwCItDk8aLG5cGtBxoBJD4FZrRh6Xmt1gKNltWQ9o/ocx5lt+C1xdOVulHGEiVtH/0kHS26xd7nz/pcaPf6gjPsEzAtV+ppeTN4eVKTHlJ0iVL2sUnE8onBanvV2A79u61HiVK7AgJu3FufVI0fs3s62m6BWZOa0/XZxXm4Z7okMCeb9Lh1SlncxyzMkV77fqdbqXseCZo8XggADDyHwgifjVJHSoIUAVHEu3KG5ZziXADAb2tb0e8fXk/IWEKCNAvwqWpqCo+QSutL/yYb5t8qTw5ZlhtsguE5Tqn1SbT4nAnSC8sK8L3KIgDAT/bVo3sYqXtWPzpYTIxneI5DhVH6HuzsG8D5n1ej0+fHAqsJ16dw8tBI4A+ERkhlQTrCEdJoBBubIj+/XxDxYY/sHznIrqfEoMNC2YLsnU6H0tCkNkIqimJ4hFSOFutE6TdEgjQ2idaQAsEaRjVi6LlmKVL13fICmHge/+vpx1MRbJXiMVy7p2hcOaEI7x41E/9dMhMWFRZ9U0wGaDnJbaJxBB1UWLq+wqCP2GzGfjPU2CSd27r9AeRqNfjtrCpMNunR4fPj0fr2sd61pCFBmgUkY/sEQImQJmv7tFWeVHPMILGXTFrFL4iodklprZkWI26eXIbpZgNavX6c8sk+nPzJXpz8yV6c8sk+1Qd2QRSVkaHkWxcOqyO9bk8dDru9mGjU49mFU9LKPzYS4T6k0mc60jWk0ThK/p5/6nAiECFq9HmfCw6/gFytRhEzoZwqp+1fa+tRvvvqBakXoiiJKg0ftH3SgQlSStnHItEaUiAohmoHvDEvkvc73djucEHDATdNKsWtU6UI5D3Vzagd8EAURXzU04+b9tbj/J3V+EtDO7oibM8niPif3GE/nIamaMy1mpCj8veu53lMMY289VVDlPpRxkKbGRyARo9P+QzHK6z+/KSCHJg0PNZNkZrSHqlvy9j3hgRpFuCPY4wviqJi+8SamoAQQZpECsYjCPhUTlUuG1TblEyEtGbAA68owsTzqDTqYdLw+L9ZVdBy0sFnd78bu/vd2NU/gAdrW1Vt86DLg15/ACaewxwVs8HHE+yAPyAIyNdp8PzCqUlbyowmfqXL3jJqXfbRmGUxwazh0RcQsD/CSZrVjx6XZ1VqT0M5TZ5J/l5XHwRIfpglKgUSi44C4cb4OlFaTpOaYhOsIVUvSHN1WqVu+PMYF9svNEu1oyfn56BEtgk7LteKAUHAxV/U4NiP9uDszw7ib82deL+7Dz8/0IiF/9uNy7+swVvtvUrGa2efC/0BAXlaDeancKBHsoyGF6tiim+MfCyyajVKc9ln4zxKyurPT5PLf84ssmOhzQRnQFB9jkw3SJBmAUHbp2iCNHj1zXFBQcoErC+JaMpOhwtuQUShTjvEpoZFSPc4B1SfGNlBbqbFqKRqjrRb8P7Rs/DCwil4YeEUPD53EgCg1etTtc8sXb/QZo45xWo8wrpVTTyPZxdMxRSVVkNjDUvPS8b4LGU/NoJUy3M4MoZBfrzu6PlWE0pDLgJmWUyq/V/Z+8BxevC8Dhq5hlQrSsupyz42wRrSxC7C4nlh+gQRL8nDDy4sKwAglcj8blYlLBoe+11u1Ax4Ydbw+E5pPm6bWo75VhN8ooj/dPTi0l01WLRlN2470KD4MS/Ps0U9to8moyNII1s+hUKNTcDhAQ/2u9zQcMBKuZyD4zjcJlt3Pd3UGTHqnu7Q6NAsIJ7tE2toAgal7GWR5k0iu7eNpetzLUNOohMMOhTotOj0+bG7f0BV/WaoIA1lmtmoGMELoggdx8Enimj1+mIetABgqzwG85gUdKdmG98uzcdBlwcXlRcozgjpjiB4ldKTsBpS/9idmJbYLfiwpx/be134boiNo9MfUMpFBtePMjiOw6mFOXhGFh5zVM6wB8LHhgIINjXJgpRqSKPjDghw+KX3J5GmJkASpK+09UQVQ+92OtDh86NQp8XJIQ2CVSYDHp0zES+3duOUghx8rciuuC6srSrGnv4BvNjShX+0dqPd68fjDcGypFTXjybLaAjSaJZPoSyymfH3lm58noS1YLbA0vVL7Vbkhkz2W55nw2yLEXucbrzX6cC3Soc31W+0oQhpFiAfW6GNchHNGpqA8KYmAxOkSZy7QhuaBsPmDgNSF7EaWEf+YEEaCs9xytScJhWG02wfj8ml+tHBlBv1+OOciRE/v3QlNBKaDjWkALBYFvODJzZt6emHTxRRZdRjkil69PnUENGSSFlJUJBKz89sn7SCtB9k+xSddjlypOc41TWUDCU6F0UMPd8iXVycV5o3JCtzaqEdf5o7CeeV5odZgAFSB/+d0yrw2bK5eGb+ZJxZlAs9x8Gm4dPG+YIdm/e73CM2FUhNhPSIkOaykez4T2eY3dOpEb4bpxVKtekbUjhcZrQgQZoFxGtqEpQIKQeOCx4I9Zz08fsS/E37BBEfR6kfZSzKSawbMlqEdDDlTJDG6fSsd3vR4PZBw0GZqkNkNqx+lOcN4HltSA3pyNo+xYJF/6sHPGEpMrXd0cvzbDDJwmVuAnWCQwSpkrKXBSlFSKPS7gk2NCU6Ine+zQQekodvy6BjUKvHh3dkEXCBnK5PFC3P4dRCOx6fNwm7l8/Dx8vmoDRNfIEnmwzQcRxcAWHIlLdUEBBF5bgeS5DOsZqg5aRRvg0j2PGfrvT5A9giB1tYHXoorKZ0Y5cjqXK8sYQEaRbgiyNIRSHYYR96AA5N2SdypfllvwuugNQ9PCuKgGQR0ljF/8r+CyIOhXTYx6JcPlDFE6Tb5B/sAqtZla0Jkf6EdtgD0vhQafnYpezzQ2qo/y7XDgLApi7p+xdv6pVZw+OhORNx29RyLEjIFH+wIJX+1QrS85IgjU4ylk8Mi0aDGfIxavCx7e8tXQiIkufxjAT8ZKNh02qQp0ufqjodz2FqgtOqEqHV44NPFKHlEFZbPRijhleyCePR/mlTVx98oogpJgOmmod+zxblmFGg08LhF/BR79hdrCcDCdIsIBCnyz7oQRp+1clGFIrglGlPamB2T0tzLRG94oBgauugy4O+OEa9hwY88IkirBoeFXGiAcEIaewr9G0xSgqIzGSwINXKIoxFTseKM4pyAQB3HGzC9XvqcMglNRxwAJbnxf/+fa0oF2urihOK1g0WpLxs+2QWpJRxh9c/btOZ8VAamgzJib1IjU2iKOIF+YIk2ehoJjCSdaQs6lpm0MecOgiM78amtzulYTGR0vWA1NzMyjxCJzllAiRIswBfnKYmQfEgDRekhpCZ2YmMD1XEnj36ybZIr0OFQQcRwBdxDhrs4DbDYox7UlYEqTt2hJSJ5mVUP5o1MOGplbvrmTAVhAGI4thNJ/np5FLcNKkUHIAXWrqwavs+AMACm2nEIlyKKf6gCGm+UA8A6AsI6M7giS0jiWL5pEsuFR5JDH3S68RBlwcmnsfZ8tScbEQRpAlO4VNDgye25VMo8dwOshVRFJWRr6dGSNczmFjNtDpSEqRZAJtHHtX2SY6Q8lz4D13PB1PZas3xA6KopAHida8HD9yxuyH3yWMT46XrAaDcED9l3+rx4dCABxykkXtEdjAkZa+xhNw3dicmDcfhpsml+PuiqSjWa9EnW51Fs3tKBcH3whT2rwEexcv08EDq6/yygbYkTPFDCRVDLAr9vBwdPas4N+2HSwyH0YiQxnNPAYLnli/6XCPWYJWOHHR50O71w8hzMYe9rMy3QcdxODTgwcERuHgYKUiQZgHxbJ9YUxPHhwtSLa8DJ0dG1RY/7+kfgMMvwKrhMS9OzZvaq1iloSlCPcxgyo3xU/asu36e1QR7GtVgEcNDmWMvC1GeNyhNemMpSBnL82x496iZODk/B2YNj3NK8kbsuYIRUibOg7/FKqP0nhwe8IzY82cyLEJanGSz0ByrEXqOQ7c/gDq3F05/AK+29QAALijLLJudRGGC9IDTEyYEu31+/PFwK3qG4X2pxvKJMcNshInn0BcQlCln44FtcjDoyBxLWIZzMFatBsfKAaNMStunXJDeeeed4Dgu7DZr1izlfrfbjbVr16KgoABWqxXnnnsuWlszc6pAuhC/qUmOkA4SpBpeDy2k+zwqrzJZKvxouyVunc8RKut81HbYA8EIabvXD2+Uxo1tvUGPVCJ78A+KkHIcp6Sqx7qOlFGk1+HZhVOwf/l8zE6gSSlRBkdIOU6jlORUGqTfJUVIIxM0xU/uYlXP88qI188cLrza3gNXQMAUkwFLszwjM8logJ7jMCAIioAEgHX7G/CLQ824r6Yl6W0nEiHV8hzm25KvI4006jcT2Nqj/tzGUvqs5jQTGJEI6dy5c9Hc3KzcPvzwQ+W+G264Aa+99hr+/ve/Y9OmTWhqasI3v/nNkdiNcUPQ9iny/WxsKDcoZc/zeuggXdFGE3eDYVdoapqFFsgHjHq3N+psXa8goGZAXYc9ABToNDDwHERgiO0KI5ZHKpG5BELGhjIUc/wx9CKNRLyLteEyOEIKALzsRVqll37Lte7xEzlKhGANafLZk9A6UjYq9IKy/IRtpDINLc8prhIskNDg9uK19h4AwH87epNuplPm2BviC1Ig+TrSBrcXi7d8hSt21SS2g2OMKIrKue1YFec2Vkf6ca9zWJHr0WREBKlWq0VpaalyKywsBAD09vbiL3/5C37729/ipJNOwuLFi/Hkk09iy5Yt2LZtW9TteTweOByOsBsRRImQRp1lHzlCyvE6aBVBqu4gsrdfOgixg0EscrQazJcjCW9HSRtUuzzwi4BNwyum97HgQs3xIwjSTq9fOVAujdF0Nd7w+/tRXf0b9PfvG+tdSZrBEVLp/8z6Kb0E6UgzeFJT6P8rdNLvgiKkQxFFMWj7FOd44/P1orr6ATid1UPuWyR7xr7R3ouPe53gAZyXYVNxkmVwHenjDe1gcxgaPT7sSaK+VBTFoCA1qRSkSXba31fTjBavD/9p780YoQYAdW4vmj0+6DgOR6rw1p5oMmCmxYiACKURKt0ZEUF64MABlJeXY8qUKbjoootQV1cHANixYwd8Ph9OOeUUZd1Zs2ahqqoKW7dujbq99evXw263K7fKysqR2O2MhR0MotaQRrF94kNS9l4VV7V+QUSdfNCYrHL2+Zlyx+m/5RqrwYSm69VGF2I1NrGGq5kWIwqSTMllI61tb6D28MM4VPN/Y70rSTO4hlT6PzPHH/sa0tFksO1T6P8rdNJvlGpIh+IMCBiQs0HxIqQtra+i9vAjqK19aMh9TAyxtPXJBTlpY2A/0oQK0j5/AM/Ko29ZM93bHYmniDt8fgwIIjgEnVTiwYIiu/sHEuqB+HtLNwBAhBQ9zBSYGf4imxlmjTrplmnd9ikXpEuXLsVTTz2Ft956C4888ghqampw/PHHo6+vDy0tLdDr9cjNzQ17TElJCVpaoteerFu3Dr29vcqtvr4+1bud0bAfY/RJTZFtn3hOBx0TpCp+0I0eL3yiCAPPqT5onCl7NH7Y04cO79Cr0UTqRxnsuRsjTAuhdH1k3O5GAIDHk3yN11ijpOxDI6Ry+t7vzywD6OESUZDKKfsKrZTOb/L44CGD/DBYdNSi4eMOzPB62gAAbnfTkPumm41hoiDbm5lCCRWkzzZ1oj8gYLrZgB9PKgWQnPhpkG38SvQ6xR87HpNMeuRoebgFEXud6uba//JQM0QA7EzJzheZwLYkrAzZ1Kb3Oh2KG086k3JBunr1apx33nlYsGABVq1ahf/85z/o6enBSy+9lPQ2DQYDcnJywm5EkHijQ6PZPnG8HpoEakhr5RTgRKMhqiH+YCabDVhgNSEgAm929Ay5f7+KGfaDYYK0OUKElP1oj8ny5oJE8XrapX+9nWO8J8mjpOwj1pBShJSXU/a5nAtmDQ8RGJERj5lMewKWTz5fDwDA420fcp+G45TJWgU6bdrMmx8NZslTkg643Hi8QXpvvldZrDTRfOpwRe0ZiEZDAh32DJ7jsDCBxqatPf14p9MBDQfcMKlEXpY5EVImnuPZLYay2G6BXatBjz8Q1w88HRhx26fc3FzMmDEDBw8eRGlpKbxeL3p6esLWaW1tRWlp6UjvStbiVzupaVANaaIR0kNyCnCyWf1BA4idtg9GSNV3JAfHh4afbHt9fuzql66UKUIajscrOVlksiCNnLJPz6amkSZyyl76DQnCACbKv5FaqiMNI5GxoT6/lHr2RhCkgGTzBQAXluWrjuplA1UmPYw8B7cgotHjQ4FOi2+V5KHMoMcCqwkigHcTjJLWKx32iZU9qG1sEkURv6iWIt0XlRXgInma1pf9LvRnwACJRrcXdW4veCTmra3hOGVa3Kbu9K8jHfFfUX9/P6qrq1FWVobFixdDp9Ph3XffVe7ft28f6urqsGzZspHelawlXoQ0mLIf2mXPmpp8KmpIa2W/t0kmdfWjjLNkQfq/7v6wtH2T25tQhz0j2rSmj3udEAFMMRlQMk7qudTikSOkgjCQsdHEQMQIqWz7lKGvKVkCgtxlzw9tagoIA5goN4ZQHWk4iZji+3xSrWEg4IxoK3ZNVTGenj8ZP51cltqdTHM0HIfpIZ7Rl1UUwiiXL7AoaaJp+0Qsn0JR29j0ZkcvdjhcMPE8fjypFBVGPaqMegREacpWusOmI863mRIevLBCvnDalAGNTSkXpDfddBM2bdqE2tpabNmyBeeccw40Gg0uuOAC2O12XHHFFbjxxhuxceNG7NixA5dddhmWLVuGY445JtW7Mm5Q60M62PYptMveoyJCysTj5AQF6USTAQttJggA/iPbg4iiiBv31iMgAkflWJSCeDWUR+my3y5fJdN0pqF4PEGv30yNksaqIQ0EMqcWLBVEtMCSxWkg4MJEo/QbTbdO+1dau/HNzw7iG58eUG437q1D7yh1O7ML4iI1EVI5ZQ9EjpIaNTxOK7RHzUxlMyyAYOA5XFpRqCw/tcAOAHi/qy+h+mX2PU0kZQ8EI6R7nW64ApGfzx0QsP5QMwDge5VFSrCCeXlmQh1pcBR24pm/E/IlQbrD4YIzzaPBKRekDQ0NuOCCCzBz5kx8+9vfRkFBAbZt24aioiIAwO9+9zt8/etfx7nnnosVK1agtLQU//znP1O9G+MGURSVLvvoEdLItk88bwhJ2cc/eCQrSAHgrGJpag1L2z/b3IX3u/tg5Dn8bnZlQv59LGXf4fOHHfTYlW6skWrjEUHwwefrUv72ejvGcG+Sxx8hZT9ua0gjREhZDakQCImQppEXqUcQsG5/A7b09GNbr1O5PdfchUt31cAdRVCkkkRM8UMFaaQ60vEMq2O8qKwAhSHv5QKbCSV6LZwBQannj8crrd14t0uKqCY6TKLcoEORXouACHzVP7SxKSCKWLvnMA64PMjXafDDqmLlPibutkWIkO7uH0irVD7z/1bjPzqYSSYDqox6+ERR6dRPV1IuSF944QU0NTXB4/GgoaEBL7zwAqZOnarcbzQa8dBDD6GrqwtOpxP//Oc/qX50GPhDApu6KJou2NQ0uMs+mLKPZ/sUEEXlKnaySp+4UM4skq6ct/T041OHE3cclLq+fza5DNNUjAwNJU+rgVGOSrDGJr8g4jM5QrrYHt8jdTwhCVAx5O8MjZBG8CFVakjTZFLTaBHpvdAoFlgDmGhKvwjpWx296PYHUGbQ4fG5k/D43En4v1lVsGl4bO1x4kd76kZ8LnlCNaRxIqTjmQvK8vHvI6bh7mkVYct5jlMavNTYP33Y3Ydr90i2kFdUFGJJTmLHbo7jgnWkg9L2oiji1gONeKO9F3qOw+NzJyEnJN3NBOlnDldYdPXtjl6c/Mk+rN6xH91p4FPa5vHhoMsDDsln/1iUdHOa15GOn0rsLCW09jOaMb4yqWmw7ROvC/qQxknZN3l88IoidByHigTTKgBQZTLgCJsZAoDv7KyGMyBgqd2CqyqLEt4Wx3FBL1K5jnSPcwADgoAcLY8ZCQrcbGfwydTry2xBqtUGowTBGtLxI0gFwa/8psOM8Xnpex8QBjBJiZB6k56ck2rYRKPzS/NxZnEuzizOxXfK8vHk/MnQcxxea+/BbQcaR3R/1UZIAwEPBCEYcWMWUISEhuNwdK414jmHpe03dDpifpa7+wdw6Zc18Ioivl5kx93TK5KadBWtsen3h9vwZGMHOAB/nDMRx8m1lIyJRj3KDDr4RBGfOqTjh18QcY/c/HTA5cHFX9RgYBQi96G81NKF7+ysxmP1bWj3+rBVjo7OsRqRm+R0sePzmCAdZxFSYnTxhwrSBFP2XAJNTayhaaJJD02S4/FYc1NfQICJ5/DgrKqktxWsI5VOzCxdvzjHotqSarwQWj8KAL4MjJAKgi9EhIV2lo+/LvtQoRQtQjrBqAcHwBUQ0JEGUZ5Gtxfvy00V3xnk2bk8z4bfz64CAPylsQN/rBs58cdqSAvjCFK/vyfsb0+GlrmMBcfnW2HgOdS5vfgqytSmA043Lvy8Gv0BActyLfjj7IlJnwtYY9PWnn78o6UL/2jpwgM1LVhfI9WN3jO9Qjn3hMJxnGIPyOpIX2zpwgGXB3laDexaDT5xOPGDr2pHzcPTHRBw+4FGbOruw+0Hm3DElt2486AkkI8ZxuTB5XlWcJBcbaKN3E4HSJBmOKGCNNqkJlE+kQ/2IQ21fYrX1MTqRxPtsA/l6yEHhZ9PLVc97SkSZcbwxqYdLF2vYqTaeGNw/VsmpuxDBWfEpqZxlLIP1svyYcMueI1Rud/A88pFWzpYP73Y0gURUg1cpGPIN0rycI+c/v3loWbUuFJf+xo2NjROyj40XQ9QhDQRLBoNjpPT4V/bsR9X767FO50O9Pj8eK65E9/49ACO/3gvWr1+zLYY8dS8yUqXfjKwCGmjx4e1e+qwdk8dHqiVBoD8qKoYV06InoVT6kh7nHAFBNxfIz3u+kkl+Ov8yTDwHN7qcOCWAw2jkml4q6MXPf4ACnVaLLKZ4ReDZWnDsTLM12mxQB53m85pe5qtmOGwKzceiBoZZBHSIT6kIRHSeGkJ5kE6ZRiCtNKoxz3TKtDh8+PykM7MZKgYND6UGpqiE4yQcgDEjGxqYrY7PK8Pi/SzGtLxZPsU6kEamuJkEVJBkKJSE00GNHp8ODzgGdPfhSCKSrr+whgTja6qLMIb7T3Y1uvEB919w7pgjUSPP6BkguLZPg0RpFRDmhB3TqtA8+5a7HG68e+2niEe1ByAlfk2/HZWJexJpqEZBXotbptajs2DbI2W51lxTUgTUySYyNvhcOKRuja0eH2YYNTh0opCGHgeD8+ZiCt31eLppk681dGrRHF5+bHfKc3HcXnWlGXlnpd/J98tL8DNU8qwz+nGyy1dcAsCVhXah7XtE/Js+LxvAJu7+vDt0vScLEaCNMOJZ/kExJrUpEMJpCvCZ5s7cdWEwqgHh1olQpp4/WgoydSMRiLoRepFm8eHOrcXHIAjEyyKHw+w6I7ZPBku16GMrCENNvGERwmCaer0ro1KJZFM8YFw2ydAKq/Z0jP2EdItPf2oc3th0/D4mjxKOBrH5VmxrdeJrT39uHiYF62DYdHRXK0GhjhG9oMFKXXZJ8YMixHvHTUTu/oH8FJLF/7R2o0uXwDTzAZ8uzQf55bkJdWLEI21VcVYG0d8RmKa2YBCnRYdPj9+I0dVb55cpnw/zijKxa9mTMDP9zco3x/Gy63deLm1GxUGHc4rzcePqoojjqPt8wfwXpcDqwvtMQco1Lu9SvTyfPnCbabFiJ9PLU/4dUViRb4Nv69rw+buPoiimFS97khDgjTDUUzxY3jhBSOkg5ua9DgN/8Em7nQ0e4pw+8Em/J9cyzWYGqXDPrVRi2QpC/Ei3S4XpM+yGGFL0DR4PODxSoLUap0tCdIMTtmHpuulv63y/eMpQipbPg0WpMwYPyBFSCcxL9Ixtn5iUZ9zSvLC5r9HQopYtWJbrzPlJ01meaNmaAYzxTcYSuHxtMBDKfuE4TgO821mzLeZcdvUcrR5/agw6NJKCHEch2NyLXi9vRcCgNkWI75Zkhe2zmUVhTi9MCdsqEuvP4B/t/Xg1bYeNHp8ePBwKxrcXvxxzsQhz/Gz/Q34R2s3zi/Nx4NRzq8A8GKzVNayPNequGSkkqPsFph4Dm1eP/Y63QlbbI0GVEOa4ShjQ5OIkPKcDgZ4sVbzDDhIdV6RrDoEUVQmvsRLox048Ct89PGZ8PtHtk6lImR86PZeSYwsoXR9RNgce5t1jvR3BgrSoAfpIBGmZRFSZ9p0k480QXE+WJDKKfuQCCkA1I1hhLTX58cb8jCMC+RxjbE4MscCHceh2ePDYXfq9rve7Q0ZHRk/XcnGhlos06W/fV0QhLFvDstU9DwvNdqlkRhlhM6G//nU8ojNVWUGvSKu59vMWJ5nw30zK/H5sXPxu1mVAIB/tXWjcdB3tsHtxStt0sXNCy1deCfKBCtBFPFCi3RcvkDF9zMZDDyvvNZ0rSMlQZrhqEnZs+5kfkiEVBKXM7AX35NT6T/ZVz/Ee63Z44NbEKHlgAmG2GmW5pZ/or//K3R2fZjYC0kQlrLv8gXwYY/041pM6fqIsDn2VtssAFL0RxTTx/RZDZEmEwHBGlJRDCjf82E/V8CNpqaXhqRt04VoEVKlqUnuwmdRltoxHB/6SlsP3IKIWRYjFtniR2TMGh5HyL/jVJl4C6KIG/bUKVZzV8RocmGwCKnFPBXSaVKEL8NKXTo7N8Ph+HKsdyPtWVVoh0XDY1VhDk7Ot8V/QAhGDY8LygpwXK4VARH4c0N4acefG9oREKWJVgBw09569ERwvfiwux8Nbh9ytPHLWoZDuo8RJUGa4bCmJm2MC89YTU0AIIpe3Dy5DNPMBrR6/bjtQGPYeqzDvspoiF0aIPiUA7mj97PEXkiC2LUamOR6nC/6pBMwNTQNRRD8SkTUap3FliqfU6YQPWVvDlknNQLm4MF7sWfvOtQefjQl20s1wfciXOBp+KDtExCMkLZ6/VHHKo40/2iVvmfnl+arjo4FO59T83k+3dSJD3v6E7Ka8/ukCKlOnw+9XqplzaQ6Ure7GTs/vwKffrYGfv/4qa9OhkqjHnuWz8MT8yYnHcFlAZ2/NXWiT57w1OcP4Nkm6dj7x9kTMdVkQIvXh1sHnV8B4Plmab1zivNgGobjQDyYQf7WHmdCo11HCxKkGQ6b1BS7qSmy7RObbS8IPpg0PP5vVhV4SMXa74akFlhTRLyGptBmmd7eT1W/hmTgOA4VxuDryddphuUAoIb6hmfwwYfL4HQeHNHnSSVenzSlieM0MOiLodNJ9VGZlrYPpuzDm5o4TgN+UDPPcPD5utHU/HcAQH//3mFvbyQIRkgHi3NT2P15Wg1s8smtbgzqSDu9fsX94swIPpDRCHpDDt/K6/CAB3fLqfpErObYBZtOmwuDXhIbrPQlExgYOAxAQCDQj7a2N8d6d9IePc8n7YMKAKcU5GCa2YC+gKCIy+eaO9EXEDDdbMAZRXb8fnbw/PpWe7A0rsfnx3/kUrkLy+OXtQyH2RYjziiy48ZJJfCNkrdqIpAgzXCUGlJVTU2RI6Qs1bnYbsFVcjrrD4eDZupqZ9iHHrAdfbshCCN7EiwPaU5YnGMZ8fqkxsZn4fW2ob19w4g+TyphHfZ6fRE4jodOJx3wMk2QKil7zdCyjFROa2pofE6xTRpwHR729kaCqBFSNsteGFAagpjn51jUkb7X5YAIYJ7VlFBH9VF2CzScVPfZMIw6UkEUcd2eOrgCAo7NtSZkNcfKNXS6XOgNsiDNpAipp0X5P7vAIkYOnuOUKOljDe1wBwQ8Lqfvv1dZDJ7jsNhuwQ9kJ4Cb9tXj9gONuP1AI67dUwePIGKOxYgFI9xoxHEc/jJvMn40sQTWNGwAJkGaBgiiiECSDRnqakijzLJXBKqg1BT+oKoYOo7Dtl6nMhu+VmVDU6i/pSh60de3W/0LSYKykHrWJSNsiO/zOeB0HgAAuFw1I/pcqcQTIkilf2VBmmH1cCwdP7iGFAC0zBx/mIJUEDxoaHhG+XvA3ZCyutRUEj1CKglzUQxAFKX9rmIjRMdAkL7dIWVZTpVnm6vFqtVgvlV6LcNJ2z/R2IFtvU6YNTx+N6syIa9InzypSafLVX47mdRpHzqdrbd3B5zO6jHcm/HBt0ryUaDTosHtw4/21KHB7UOBTotzQ7r2fzKpFDPMRnT4/HisoR2PNbTjbTkbeWF5QVo2fY0mJEjHmLc7erFwy26cseNAUjNzgzWkMVL20UaHhghUduItNejwjZJcAMCj9dIB+JBLZYR0kOF6zwin7cMipPaRbWhyOHYq/3cNZJAglS2fDIYSACGCNMPM8ZnxvVYzVJAq40OHOa2ppeU1eL3tMBhK5TIAAW730HqvsSboQxoeTeHlWfbSOrL10xg1NnkFAe93yYK0MDFBCgDLcsNHOibKIZcHv5RT9XdMLU/YRic0Qqqk7DPoN+PxNIf93dz88hjtyfjBpOFxaYV0fH1Ndpa4tKIgrCbUqOHxzILJuGFiCa6tKlZud0wtx8UjnK7PBEiQjhFeQcAdBxtx8Zc1aPf6sbPPhXtrmuM/cBBqbJ9YhHSoD6luyDoA8P1KKa3wensP6gY8wQhpXEHKUlrSvvSOcGMTSwNqOOAI28gK0tCa2EyKkLKUvWFQhDTT5tlHM8aXlrEIafI1pKIooq7+LwCAygkXw2yW/ARdA+mXto9mjM/zOqUuXDHHl38jO/tcYT6KI81HPU70BQQU6bXKaMdECB3pmCgBUcT1e+swIIhYkWdN+EQviiJ8rKlJlwe9QToesou7TIBFSPPzjgMguZ+EHuOJkUGa8CSd/ww8h0sjlIlMNBlw85Qy3DK1XLn9oKo4pmn+eIHegTHg8IAHZ396EH+qlwTcankk2GP17QmnqBKyfYrS1AQAghhM6c21mnB8nmRj8atDzRgQRGg4YIIx/PGDYV2odvuRAKRO+1R7Q/p8DsUPcKpcQrDIZo44ISOVhIprn687Y7rU2UlUzyKkmV5Dqh0qbrRKDan6347X2xnmK9nV9QGczv3QaCwoL78AJpMkSAdctcPY65EhmiCVlrF59lJaf4ZF+nuHw4VFW3bh0i8P4T/tPUpmZaTYIKchTynISWqs4lK7BRyA6gEPWj2JCanH69vxca8TVg2P38yqSjgNGgi4lJIHrdYe0tSUQYLULdWQlldcAJ2uAF5vBzo7N43xXqUOQfAgEBjbgQ+RKNLrlLGc3ynNR5E+/hAGIggJ0lHGIwg457OD+KzPBbtWg6fmTcaT8yfjgrJ8iACu31sHZ0C9R6RfzehQkfmQDhaknJK2FwfVyrEo6SvyDOIJBn3cKziW0iosOAkcp4XH2wq3u0nlK4mP292MD/+3DF/u+iEAqRv3T3Mn4qEI0zFSiSgG0Cun7DlOEr6uNBQqkfAMiZBKV+xeX9eY7VMyMLEZMWWvTWxaU2vbf/DBh8fgf1uOw4EDv0Jf/17U1T8BACgvOw86XQ7MpsyLkAJB6ydB9iJdarfggZmVWGgzwS8Cb3U4cPmuWvxoz8i9LlEU8XanFGFMtH6UYddpMVdu8NjWq/5C44DTrWSa7ppWgcokxlOydD3H6aHRmJWmJk9GpeylCKnJWIGy0m8AyJ7mJlEUsePTC7F164lpaWl117QK/H52Fe6cVjHWu5JxkCAdZT5zuNDk8SFfp8E7R83E6UVSdPSuaRUoN+hQO+DFr6rVp+4V26cYn6SSsueGXq0N7rRnnJRvwwxzsCZNzchQJkhNpgmwWmcDAHodqasj7en5BILgRnf3NqWL+OziPKVObri4XDXYs2fdEBHtdB5EINAPjcYMu32xsm4m4B2FGlK3uxm1tY9g777blFRnqgmKsEgpe9l/U46iBgID2Lf/TrS2vjFkXY+nDXv33gpAgNfbgbr6v+Djj89AV9cHAHhUVl4KADCZJwFg9jnpRSxByg+yfuI4DmvKC/DfJTPx/tEz8cPKYmg44F9tPfgoRT6fgzno8qB2wAs9x+GEvMSMxkM5Jle9/ZMgivhfdx9++NVhuAURJ+bbcGGSE2/8IQ1NHMeF1JC2ZcQ0MEHwK9kqg6EMZeXnAQA6OzfCk0HWVdFwug7C4dgJj7c1La3ZzBoe3y7NjzsmlxgKvWOjDCvSPy7XFnb1nqPV4LfyCLK/NHbgfypHe70mRzDzddqo6wSbmoZGC6IJUo7j8P3K4EQTNf59rIZUry+C3X4EgNTWkbIu90DACd8IRPgOHLwXTc0vobr6gbDlrH40J2chLJZpAACX61DKn38kULrs5SiPTp+alL0geNHS8io+++xi/G/L8ag+9AAaG5/Dvn23D2+Ho6DUkEbosg/WkErrHD78GBoansGu3dehre2/ynqiKGLvvlvh9/fCZpuLBfMfRVHRKuVCraT4azCZpN+gEiFNQ+unaE1NocuYIA1llsWE26eV40J5hOcvqptHRGCxruHj8qzDKqVhdaSxGpsa3F7cV9OMpdv24Nyd1fiyfwA5Wh6/mVmZdMdysKFJChbo9VK2SEoTp19EbjDScVgAx2mh1xfAapmOnJwjIIoBtLS+Mta7N2y6QqYAZkqmilAHCdJRhhXpsy7SUFbm5+C7cgH+j/fVwxtnksKrbd14rb0HWg64bmJJ1PViRkjlZUy0hvLNkjwUykI3nik+AOXqWxKkUh1pKg3ymSAFgIGBupRtFwD8/j50dm4GALS1vwWfLzgYgIlqe84RMJunAMiMA2HolCaDfFJlNaTDHYO4b/9d2P3Vjejq/h8AEXb7YnCcBq1tr6N1BIy4/f7Is+xDl/kDTnjkqKeEiN1fXY/unk8AAC0t/0JHx7vgOB3mzL4fRUWnYsH8h3H88q1YMP9PmDXrV8o2WQ2p292QdjPMg4I0kjiXBakQvXzhx5NKYeI5fOJwKuIxlWyQTb5PSTJdz1hqlwTpPqcb+53uIffXDnhw4sd78dvaVtS7vbBpeKwpK8BrR85AeRKpekZQkEp2PRqNEVqtFOnNBOsnj+xBatAXg+OkU3y5HCVtaf7XmO1Xqujq+p/y/0wJDBDqIEE6ivgEEZ84mCAdmnoEJIuSYr0WtQNePNMUXTS0e31Yt78BgCRGF8ToZI1m+wQEO+8j+S0aNTx+PXMCludacU5x3pD7QwkE3Er0wGAogj1HEqT9/XsUC5rh0h8mSOtTsk1Ge8e7Sq2tIHjQ2va6ch8rO7Dbj4TZNAnAyFs/CYIf+w/8AofrHle1vt/fh6+++glaW4P7LYlOAQCvpOrZv4GAK+mudFEUlOEAlZWX49hlG7Fk8UuYOPH7AIB9+24PKwkQBD9qah/C7q9uijosoa7uLzhYfT9EMfJFWLTRodKyYA1pbe0fEQg4YbPNR2HhKRAEL7744mp0dn6A/QfuBgBMmXwdrNaZyuN1ujwUFZ2i+JkCUokDzxsgiv60s34K+pBGiJDKU6uEGL+5UoNOGYDxy+rmlDY4dfv8yjEu2fpRRqFeq2zjuj11YfspiCKu31OHvoCA2RYjHpkzEV8cNw8PzKrETIsx2iZVoQhSrV1ZplfS9mOX8j5c9zi27/h22O1g9f1D1mP1o6xMBwCKCk8FAPQ794VdbGcaguBFT89Hyt+ZZMFHxIcE6SjyRZ8LroCAPK0m6kHTqtXgx5NKAQC/q21Fv39og5MoivjJvnp0+QKYZzXFjI4CIcb4EVP2urB1BnNGUS5ePmIaig2xuwWZAOF5PTQaK4zGcuj1xRBFPxx9X8Z8rBoCAU9YVDTVtX1tbf8BINVcAUBT00sAAK+3S6kXtdsXwWyeDECKkEYTT6mgru7PqK9/EgcP3quqLrOm9iE0t/wT+/bfqUT0gqb4hUozlkZjBc9L5Rdeb3JlD/39++DzdYLnTZg29SaYTFUAgMmTroHVOgs+Xxf27rsDoijC7W7Gp59dhEOHfitFKDvfH7I9r7cDBw7+CocPPxo1oh7T9knuvO/v34vGxhcAANOm3Yx5c/8PdvuR8Psd2Pn5pfD7+5CTsxBVVVfFfY0cxyuvK93qSP0xxHmwhjT2xcbaqmLkaTXY73LjpdbUlb9s7OpDQARmWYyoSkFt969nTECOlsdnfS48Uh+MTv6lQTK9t2h4/HX+ZJxTkroZ4MrYUF3wInyszfEDAQ+qq+9Hb++OsNvhw4/C7Q7vOXDLHqQGY5myTK/Ph9E4AQDQ17dL1XP2Ow+kZBxvKul1fB62T5lSy0+ogwTpKMJqoZbmWmJaoVxYVoApJgM6fH48Wj/0ivyfrd14q8MBHcfh97OrYna/i6KgREhjNjWJQyOkiRBaPyp173MpTdu7XNWQon0SqYyQ+nwOdHZ+AACYO+cBcJwOfX1foq9/r2KIbzZPgU6XB6NxAjhOB0FwK6mxVNPfvw+Hav5P+TvUlD8SbncTGhr+Kr+WbnT3bAMQaopfrKzLcVyI9VNyjU1d3VINV17e0Yq4BaTv0pzZ94HjtGhvfwsHDv4KH39yJnp7twcfG5JuCy7bovy/tW1oI5Ig+JXIqjbSpCZZmPX17YIo+pCffzzy85ZBozFi4YLHYTZPC9s/no9ebx1KMBpeq2r90YJ10LNoaChKg1eEGtJQ7DqtciH7QE1LUkM5IsHS9acNMzrKKDfqcbfcrXx/TQv2OgdQ7XLjV4eCpvepEL6h+PzMgzRXWWYY4wipy1UNUQxAq7Vh/ryHMX/ewzAapffF6QqfwhQpQgoAOTkLAEBVgKC9fQM++uh0bPvo9BH3k04EVj+ak7MIgHSxyKYMEpkPCdJRZGtP7HQ9Q8dz+NkU6er2kfo2tHuD0csv+1z4+QEphfjjSSWYE2f2bWhtaMSUPashHaZpMhM3LJIAIKSxafiCNLR+FAAG3KkTpB0d70AUvbBYpiMv7xgUFp4MAGhu+ruy7/Yc6bXwvFaJnA3n6lwURRw8+Gvs3n1j2Jg/QfDhqz0/VcoHgPiNYYdqfh9WctEmd5cHTfGLw9bXDXN8KBOV+XnLh9xns83FpElrAQD19U/A5+uGzTYP06f9XH7sh0MeI9Whyvve9taQE0zoSNDI3pvhInXa1J8q/9fpcnHEoidRUnIW5s59UGlKU4PJzLxIwyOk3d3b8NlnF49JdEYUhWANaaQGL3laU0CILUgBycS7wqBDk8eHJxqH77rgE0S81yU1Y55aaI+ztnq+U5qPUwpy4JVn01+/p14xvf/uCEy3YRFSbYggDZrjp06QNjf/C1988X34/fEbWNnxz2KZieLiVSguXgWbdQ4AwOUcLEjlGlJDadjyHNt8AIDD8UXc56uXL3Dd7kbs+PR8HK57fEQzQmphx57ysvPAcXoIgndIhJjIXEiQjhIBUcTHsp9ePEEKAGcW2bHIZoYzIODB2laIoognGtpxxo4D6PEHsMhmxjVVsVP1QHgqnuPUd9knikeJkAYnU+QqEdLhG+Sz+lGbdS6A1DY1tcrp+uLirwEAysu+BQBoaX0V3XK9Eov2AghJ24cLEo+nHT29O1Q9Z2fXJhyuewwtra/io4+/rphWHz78J/T17YJWa8ekSdcAiC3o+/v3o7n5HwCAqVN+AgBoa38bguAL6bAPF6TDmdYUCHjQIzcJ5ecfF3GdSRN/gJychQCACRMuxpLFL6G8/DxwnAYDA4cxMNCgrCuKYphI9Xrb0NMT/h4ywarTFUQsOwkVpKUlZ8NmmxN2v9FYjnlzf4fiolWJvFSlsWmwF+mhQw+iq/t/OFTz+4S2lwoEIVgbOpwIKSDViN80WRItTzS0IzDM3+gnvU70+gPI12lwZE7qJqdxHIcHZlbCrtXg874BfOKQTO9/m4TpvRr8vhgR0hTZJomiiIPVv0Z7xwa0qWgCZMc/q2W6ssxsmQog8QhpXxxBOjBQj+7urQA4FBacBFH04+DBe/H5F1cr9bVjgc/ngMPxOQCgoGBFcJoaNTaF4XQeRF/fnrHejaQgQTpK7O4fQF9AgE3DK4bPseA4DrdOlaKkTzd14rtf1uCWA43wiiJOK8jBcwunQMvHPxiHR0iHpipZl/3wU/YsQhoUpDbbXHCcHj5fJ9zuhmgPVQWLELDopcfTkpJJHT5fryKIiotXAwDy84+HQV8Cn687GCGVo70AYJY9KgcL0i++/AF27Pg22tvfjvmcoiigWm5G0Ggs8Pm6sPPzy7FnzzrU1P4RADBzxh0oLjodgFQ3FS0tVX3oNwAEFBWtwsSJV0GnK4Df34Pu7i3BlP2gCOlwpjX19u6AILih1xfBYpkRcR2e1+HII57Hscvex8wZd4DnDdBqbYpIDY2IulyH4PG0gOf1KC4+A0CwnpdRVyeZ1ldUXBDx+Zg9D8fpMGXKDQm/pmgw66fQGlKfr0e56Ghvf2vEfFejEXw+LmJTE6shFVQIUgD4RnEe7FoNGj0+fNg9PEsjZoZ/ckEONCkWiqUGHX45PWg0fve0CkwYRid9LJQaUm2usizVTU1O535lW2pS4sEIaVCQWsySIB0SIZWnNBkNZWHLbba5ADi4PU0xy3Wa5Ln3+fnLsWDBY5g58x7wvB6dnRtx4OD6uPs6UnT3bAUgwGyeAqOxPFhSQ3WkCj5fNz7Zfi4+2X42Ojo2jvXuJEzKBen69etx1FFHwWazobi4GN/4xjewb9++sHVWrlyp1Bmy2/e///1U70pawepHj7ZbVR+sl+fZcGK+DT5RxDudUs3o3dPK8df5k2P6joYStHzSKI0tobCI0+BJTYnCDq6GkJQ9zxtgUaKJw7uKdTr3AwByc49SGlvcKUjbt3dsgCj6YLHMUKIPPK9FWdk3lXU0GmvYicBskl9TSIeny1UDh0M6sRysvj+mVVBL67/R378XWq0Ny47ZgIqKNQCApuaXIIo+FBWeipKSs2C1zoBGY0Eg0A+n8+CQ7fT0bEdHxzvgOA2mTrkJHKdRRHVr23+CKfshEVI2rSlxQcrEZH7ecTGjUxqNQfH0ZLAUf2hElP3fbl+ivOdt7W8qAryndwccjs/A83pMmPDdiM9ls81DVdVVmDPn/iHPORxM8glvYKBe+TylSLaUuhQEL1pbX0vZ86mBTQyzWmYolj6haFQ2NTFMGh7fLJGad55vHp4V2DuyhdRpBalL14dybkke1k0uw02TSnFBkqb3avCFGOMzDCmeZx9aS93rUCNIpeOfJU6EVBRFeLwsZR8eIdVqbYptncMRuY5UFANKxqW87FvgOA4TKi7EgvmPApBqS8fKBo3VmrPMjJKpok57hYbG5xAI9EMUA/hy14/Q27tzrHcpIVIuSDdt2oS1a9di27Zt2LBhA3w+H0477TQ4neHTNq666io0Nzcrt/vuuy/Vu5JWMP/RYyL4j8bitqnlsGp4VBn1+PeR03F1ZXFCaapYHqRAbNunRIhUQwoAJvmg4RyGIA0EBpQmJot1Rkj38/AFKYvGlcjpekaZnLYHpOho6Mlf8SJ1Bg+ErSFRPZfrEJrlKMNgBMGDQ4d+CwCYWPV9GAwlmDXzLsyb90dotTYYDKWYOesX8oWaRkmzDS4FkFJ+98n7eh4slilhr6O9/W3Frihayj6ZpialfjRKuj4W7DHd3VuVerSubnaSWY78vGOh1drh9XYoZQF1dZKnaGnJN2AIib6HwnE8pk/7GUpLzkx4n2JhNJaB5/UQRR88cudyR8d7AIL1eaM9jrGn52MAQG7e0RHvZ2l8NTWkDCbu3uzoRbcvObFxyOXBQZcHOo7DyvzkpzPFguM4XDepBDdNLh2RVD3DFyFlr1zEpWjCGWsMBKToZ6xI++DjH8MiH4e83nbFysnn61aO5YMvRAEgJyd2HWlX1xZ4PM3Qau0olK2iAOn3qdXmwu/vTam3dCKwi9f8PCZIM8cTejQQBA8aGp4BABiNFRCEAXz+xZUZFUFOuSB96623cOmll2Lu3LlYuHAhnnrqKdTV1WHHjvATqtlsRmlpqXLLyUlNV2Y6IogitskR0mNV1I+GMsdqwo5lc7D1mNk4Iom6rOAc+8jprWCX/TCbmpgpviFcNITaJCWLFAEQodPlQa8rUKJgw7Xj8fl6FIHF0sUMs3kScnOlk35o/ah0n/SaBtwNSvc3ayTKkZufamp+H7GOr6HxObjdjTDoS1BZeYmyvKR4NZYftw3HLP1vmPBiz+0YlNbr6fkIvb07wPNGTJl8rbI8N3cJ9Poi+P0O9DulzETUpqYEU/Y+X7diGZOMIM3JWQiNxipvZzcEwYfu7m3K9nhej6Ki0wBIAt/lOqyUP1RWXpbw8w0XjuNhNMoNbAOHIQg+dHZJtb4zZ94tuzHsGtV6LUWQ5i6NeH+sSU3RmG81Ya7VCI8g4p+t3Unt1wY5Xb8s1wLbMKYzjTWiKIQY4+cqy5m4CxV8ySIIHnR3S58jq/mN5aQx+PjH0GptMOilKKhLjpKy+lGdLj/MAYORY2Od9pEFaVOzZHdXWno2NJrg4zlOg8KClQCAjs53473ElDMw0IiBgVpwnAZ5eccAiF7LP15paXkNXm87DIZSHH3Ua7DZ5sPn68ZnOy/NmJGxI15D2tsrHajy88NTLM8++ywKCwsxb948rFu3Di5X9BSTx+OBw+EIu2US+5xudPsDMPF8TAP7aNh12qRrstjBM1qElOdS1dQ0tIYUCNZbDgzjoOHsD6arOI5LSYRUFAXUHn4EouiH1TpbiTCGMnvWr1BVeQUqJ1watlyvL5TLBgQMDNTD6TyIfuc+cJwOC+Y/AqNxAjzeVtTX/zXscX5/H2prHwIATJ5y3ZAaQGkiTPgFC+vuH5zWa2qSInNlpeeEpeaktP3pYesOSdnrkmtq6ureCkCExTJ9SDpQDTyvU04mXV3/g8PxOQKBfuh0eUrHMIvwtrW9JU9cElGQvwJWa+R61ZHGHNJp39u7A35/H3S6fBQWrERR4SkAEDUangyBgAs7P78C23d8Z8gQAZ+vB/390kVGbu5RER/PBI7aGlJAijxeII8TfaE5OU/StzukY/KpI5SuHy38/n6wkgxtSA2pVpurHEOjRUlbW9/A/7acELeGvLf3MwjCAHS6AuUCrCdG1NHZH6wfHRwZHpy2Zx32g+tHGYr1k+OLIY2mPl832tvfASB1sQ+msPAkABiT2kQWUc7JWahMzWLnFre7MWXDVzIVURSVCXWVEy6GTmfHooV/hslUBbe7AV98cfWIjAlONSMqSAVBwPXXX4/jjjsO8+bNU5ZfeOGF+Nvf/oaNGzdi3bp1eOaZZ7BmzZqo21m/fj3sdrtyq6xMXZ3YaMDqR4+ym6FT0YiUSoQYU5oAgOOHb/skiqJykDYMStmn4ip2cEF/UJAm12nv9XZg5+eXo67uzwCAivLIzTJm82RMn34LdLrw6D3HcWGNTWxUZn7+cTAYipTGmsN1jyoNEoGAG9XVv4HP1w2zeSrKSs9Vta+smcrlqlGM7H0+B9rapecsL//2kMeER3t56HTh1jjJ1pAqKbP8oXZPasnPO1baVveHSnQ6L2+ZUhKRl7cMOl0efL5ONDY+BwCoqroy6ecbLsFO+1olXV9YsBIcp1HGMTa3vBJ1AlUiCIIfu3Zdh87O99Hbu10ZZcvo6dkOQITZPDVq+QKvUW/7FMo3S/Kg5zh82T+AL/sSM0Pv9fnxkewgcmphZme62O+V502DIoSc8ruJZv3U2PQC3O4G7Np9nRIBjUTwd3Qc7PbFAIZmQEKJ1NDEGNzYFLR8inzBaLXOAcdp4fN1we1uCruvpeVViKIXNuvcIU4VgNTZznFauFzVo54mD6brg8cena5AFqdiSodXiKKIlpZX4XKlbpsjTVfXh3A690OjMaNcPp/p9YVYtPApaDRmOPq+UBwK0pkRFaRr167Frl278MILL4Qtv/rqq7Fq1SrMnz8fF110EZ5++mn861//QnV1dcTtrFu3Dr29vcqtvj61YyNHGrX+oyMBE5p8BMsnIJjiG46/XiDgVMy6B0dIWZ2T29OUUBoxlP5ogjSJpqbu7m346OMz0dX1AXjeiNmz1qOi4sKEtxOsX6pBm2zmzqJ7pSVnwWqdDb+/D/v23409e9bhgw+XoqFRqu+ZOvXHqs3ZdbpcmOWTDkvrtba9DkHwwGKZAZvsLRhKrn2xksrT6wuGPFewhrRLtam0ZM8UbGhKFiZme3p2oKPj3bBlgHThxKJGgAirdRbyZBE7FoR22nd0SoK0QI4U5ecvh8FQCr+/B+0dw0tjiqKIfftuU54DCK9LBkLT9ZGjowCg4dXbPoWSr9NidZEU3Xw+wSjpxq4++EVgutmASSk2qR9tIjU0MVimwRthWpMoBpQTviB48cWXVyvR7MGE1mErw0NiOGnEEqTBCKlUo+9mgtRYOmRdQGo2tFqksbmhaXtRFJV66LLyodFRQCoRYN+9js7Ri5IGAp4wEc+QAgOpryPt7Hwfu7+6Ebt3X5+ybY40LDpaVnZeWADFbJ6oONMMdi9JR0ZMkF5zzTV4/fXXsXHjRkyYMCHmukuXSvVQBw8O7SQGAIPBgJycnLBbphAQRSVCeswYCFKlqSlKhJSlUDvaNyQd0mcd9hqNdYhxuU6Xp6S+Bvs5qsU5yIPPZGQ1pHUJ7XNX1//w6WffhdfbBrN5Go5a8k+Ul387qQYJFvltb38bTucBcJxOaQLgOB7TpkqeoK2t/0ZT80sIBPphNFZg+vRbUVR4WtTtRoJFSVlar1lO10vm0EP3neN4pds+UmNDcCSioNpXcGCgDm53AzhOp9TWJoPZPAUGQylE0Yu+/t0Ahhrsl4REeKsqLx/RBpZ4mORIeE/Px3C5asBxOhTIAprjNCgrlZwB2GeSLIdqHpTr93hMmvhDAEBHx7thqchuWZDmRakfBUJrSJ1R14kGa276Z2s33AlMblK661Nohj9W+JX60bwh9ynjQyNcvPf370cg4IRGY4XdvgR+fx92fn75kCikz9ejTErKz18Oq2U6NBorAoF+5cJ7yLYjeJAy2AU/c+FQIqT66CU1NrmxKdSPtLd3B/r794Ln9TGbAwsLWNp+9OpI2zveht/vgMFQFma/B4Q4nqSwjpQ1bTn6vsgI0/3+/n3o6voAAI+qCLX2LFDS2vaftBhuEIuUC1JRFHHNNdfgX//6F9577z1Mnjw57mN27twJACgri1z3ksm82d6LDp8fedrUmkWrRYyTsi/IPwEajQVuT1PSIf1g/WjkqSnDSdv7/U7Fw5RFCIzGcnCcBoLggTcBG5b6hqcBCCgqPBVHH/UvWK0zE94fBvPAY7WdBfnHh12Z5uevQHHx16DRWFFWei6OPOJZHLvsfVRVXpawwFLqSHs/RX//Pjj6vgDH6VBaenbUx1RUXAi9vljxMg2F53XKRYLaxiZm92S3HxFxfKdaOI4Li4iaTJNgMlWErZObuxQ5OUcgx7YAJSnunE8UFiFl03Tyco9WatiAoBtDZ9cHQ8SHWhobn0et7D87a+Y9mDLlRhgN5QgEnEoTld/fh74+ScDHipCGlrO4Exxte3yeDRUGHXr8AbzVoc5fNSCKeLeT1Y9mTqAgGsGGpqHiOpY5fnCi2yIsXPAnWCzT4fG0YOfnl4dd9LE6bLN5GoyG0jAnjUjd64GAS7G3i5iylyePud31EARP0BQ/SoQUGDpCNBDwYM9eaZJaSclZEaPDDFZH2tPziaoJU4yBgQYcPvynpLyjm5ukGu2ysnOHWBcGS6fUu7iIYgD1Dc+gu/ujiPf39X+l/H80I8HJIIoiDh9+DABQXLQqou1dvnyO93ia446hHmtSLkjXrl2Lv/3tb3juuedgs9nQ0tKClpYWDAxIKaTq6mrcc8892LFjB2pra/Hvf/8bF198MVasWIEFCxakendSQrXLjWZPck0/f5Jn0V9SUQhDjJnzI0W8piaNxjjskH40yyfGcBqbWPeoTpevCF6e18FgKJe2qbKxye/vU2rypky5IeIIykRgIptRPMg2iuM4zJ/3B6w84XPMmXMf8vKOiegbqQal097xBRqbngcgDQiIdgEAABbLVBy/fCsmTfphxPv1CY4PZS4Cw6kfZYSm/CNtj+e1OGrJyzjqqH9FdYcYLQyGsrDfDjshM8zmiXLHuygPKUgMt7sZ+w/cAwCYPOlaVFScD47jlAh3W6v0m5TEigCTsQpGY/QLd4OhGHb7EumxKiYAhaLhOHxHjpI+Ut+mKvuwvdeJbn8AeVoNluQkf6GSLvhiREhZXWYk8dPrCA7Q0OlysWjhEzAYSuF0HsDnX3xPiXR3R7BNU9L2EQQpi3yGHv9C0euLodFYIYoBuFyHo44NDUXptHd8CVEUUFPzIFyug9DrCzF92s+iPg6QjuVm8xSIon9IjXMsf9K9e3+Og9X3ob7hqZjbH8zAQKNyMVweYsUX3J/EvEhFUcT+/fdg//478eWuH0X8jvf1hQjSjveG3J8u+P392P3VDWhpfQUAUFV1ecT1NBqD0oA5uAwo3Ui5QnrkkUfQ29uLlStXoqysTLm9+OKLAAC9Xo933nkHp512GmbNmoUf//jHOPfcc/Haa6NrMJ0IvzrUjMVbvsL5O6vxz9ZuuFSms3b0OvGJwwk9x+HyishNCCNNMEIa/cQ+3JB+JFP8UJS0UhJepP0RDKEBJGz91N7xLkTRC7N5atQJQ4kQKkg5To+iolOGvc1oWCzToNXaIAgDaGyUBGmkg3MisFpfNZ32fX270d2zDRynRVnpOcN6XgDIzz824v/TEZ7XwmQKlhwNFqQA5PIMHi0tr6C9fUNC26+p+T0EwYNc+1GYHGLfVVwilS10dL6HQGBASddH8x8NJehUkPjJ57KKQlg0PD7vG8Br7bGjpL0+P35XK0XkTirIUTU5Lt1RBKl2aIQ0L28ZACkaPlh8sWlLLKVsNJZj0cInoNXa0Nu7Hbt3Xw9RDCj1owUhF2LsMZEmNsWqHwWkC1+LXEfKpp4BgDGGILVYpoPnjQgE+tHS8i8clps7Z838RUQhPhil216ud/b7nfjqq5/i/U3z0BJhUITX26n4DXd0vBN3+6E0t/wDgIi8vGURo3+J2goePvyoUsvv83ViYCD8cR5vR1jWrbt7S9K9DyNJX99ufPzJWWhtfQ0cp8H0abcMsScMpVg5JryZ1mn7EUnZR7pdeumlAIDKykps2rQJnZ2dcLvdOHDgAO677760rQsVRBF9/gAEAO939+GHXx3Gwv/twu0HGuETYkcQHqmXvtjnlOSh2BA5QjnSsAgpHyVCCkjpZY3GCo+nWdXUkMEoHqRROn9NKg8aknXFk2hofE750Sj1o4NsfxK1fgo1wE9FTaJWa4Ve9vcsKFgRlsZNNRzHIydnEQBAFP0wGEpRULBiWNtMxByfmdMXF38NRmP5sJ5Xeu5ClJefD7v9SBTkHz/s7Y00rNPeYpmufO9CsduPwMSqqwAAe/fdqnRqx6PfeUAZ0zht2s1h38sc2wIYjRUIBFzo7NykqqGJIdl+cejt/TThMoIivQ4/rJS+1+sPNUU9xn3qcOKU7fvxfncfdByHi8ujR+vVIooimpv/hfr6p8bMosbnl8eGRkhbS+Uqsjl8yHHS6+1QLoyZDzEAWK0zsWD+Y+B5Pdo7NuDLXddgwF0HjtOG1WGzkpyBgVrFSYMRrB+NfhGtTF/q+1JJo8eyZeN5rdJFv2fvLQAElJZ+A0VFp0Z9TCisjrSzcxP6+r7CJ9vPQXPLPyCKPtTWPjTks2tr/y+YlVZv72eqy4REUVAs1SLZUAHBaWo+X1fcevim5pdRfegBAFCm/Q2OSvfLZTFm81TZXN6jiOl0oaXlVXyy/VsYGDgMg6EMRx75PKqqroj5mIKC4+VzfMuYDTZQA82yjwPPcXhp0TRsO2Y2bpxUgkqjHn0BAY81tOO5GKP2Dg948B85wvD9ysiRw9EgXlMTEB7SZylChiiKyhSQaESaYx+K2hrS5ua/48CBX2Dfvtuwc+dl8Hg7QiIE0QRpuPWT19sxpFtVStd/AGBoan045NgkK7PSkrNSts1ohF79lpWeE3EMbCKonWfvdjejVXYRqKqMnBJKhtmzfokli/8+7NKJ0UCaAQ4UFa2Kus7kydfBYpkOr7cD+/bfpWq71dUPABBQVHTakGYNKW0vfVebm/+hjHqM1dDEMBhKFOHa1vaWqn0J5fuVRSjUaVEz4MWzg45xoiji0bo2nPXpAdS7vZho1OO1I6dj6TAbNn2+Xnz55Q/w1Z6bsP/APUrt7GgTnNI0NFIYZg4f0tTDxjNaLNOHWMTl5R2NuXN+B4BT/ElzchaF+Q3rdHaYzVIt6OCAQLwIKQBY5Mey6KtGY417gczS9qLoh15fjBnTb4+5fih2+2JotTny3PRvwOWqhkFfAp43wOk8MMR0n5X7SIjy+N34dHdvhdvdCK3WFvW3p9ValPKEWAGPjs73sXfvLQCAiVVXo6JCskYa7P/K6rRttjkhvqujPwggGoLgx779d0AUvSgsPBlLj34NubJ1WCx43qBccKRz2p4EqUommQz46eQyfPT/7d13fFPl/gfwT07SjDZJZ7obOijQUlqwUCh4BRWtiFxEXCBeEK64t1xBUfzpVdziXihc9LpQcF5RhouCjC7pAGlpaWnT0pnuNuP5/ZHktGnTNF1Jab/v14s/mnNycvKQnPPN8zzf7zMjBg9FmL4A75ZUwtjDL/nNZyphBDDHW4EYuczmPs7gyJA9APgHdO/SNxp1+PPYrfh9f5LdDzEfkEp6mEPKJ4bU9dh71Npahr9OPmX+i0NN7X4cPnwFtFpTolX3IfvuAWlNTSr2p87sNjeosnIPGGuHh0f0oBZYHz/hScTHvzuoQW5PPDv1vAQNcLge6FitqbTsU+TnP9tjhm/Jmf+AMb050ah7ianRYIz6FsTFvYaI8Dt63EcolCA25jkIBEJUVHzbayBYV3fUPHzJISryQZv7WKoNVFXvA2M6SCSBkErtVyyx8O80DaevPERC3B9u6mF7sagcTXrTD7wanR7/OFaIxwvKoGfAFSpP7J42HpMHmKyp1Wbg8JEFqKzqmO5gSWRxNsv1SWQjqQmwXRzeEkR2/o525u9/GcaN28D/bWvedE/D9g4FpOZFPSyrqDmyaIWi03c5ZsLTNpO4esJxIvj6zgZgShDy9bkASUnf8gmUnStOtLVV8tNNAgOvBOD4vExLGaqAgL9DaK6va4slwbSnxKbW1jJkZ98FxgwIDLgSUVFr+PbuWv/VMn9UIY/tVFHg52FTVL6+IQt6fQNEIi/ET3rLoSkWFpbryXAetqeAtI84gQA3h6qgFHEoaGnjS550ptXp8bG5lt8tfewd1enqkJ//HMrLvxmU8+1tLXsLX5/zTV367RXQatPBGMPx4w+jqmoPGNPh+PH1aLNRfw/oKIPS0xxSoVAGiXnlEFvzSBljyMtbC4OhEZ6e52F60nfm3qZK6M11AbuWPOHnkJozUBkz4OTJp8CYAZWVP5rnHplYhusHO3CUSgKh8rvYKWWJvL2T4Ot7IdRhq/gksYFQ+V0ENzcf6HQ1OF38Lg4dugxHjiwyZwGb6PWNKCsz1RDubUhoJBOJPBDgf3mvP+qUyniMUd8CADh+4lFUnP2fzaxixhjyC54HAAQHX8PPAexKoYiDTNoxRcDba7rDnzVTcCBAfX0GWlpKHXpOZ8uCfREuE6OyXY93z1TicF0j5h45gd3V9ZBwAjwzLhTvTQyHcoDLhJaWfoK09OvR2loKmUyNmJhnAQCVVXu6DV8PlMHQjJP5G+3+WLCX1AR0Lg6fzxdO5zPs7czhCwu9EWOj/gW5PJYvFdZZR0Da0WNnqjBi+r+Ty3sOSC29q4ApaLI3f9TCz/cieHomIiL8Lvj5Xdjr/l2FhS6Hh0c0xkY9hISE9yEW+/L1S8srvuXnXVaah+uVygSEhZqWSjbNwbWfJKzTac3P7X2+fG8jcKcKX4HB0AxP5RTExGyEQMDBU2n6v2ps+suqWoClFJ1CMRFeXtMhFLqjvf0sH+y7WkcN25l9HiXz8ZkFkUiB9vazqNOm9f4EF6CAtB/kIiFuDDYNT1vmiXb2YVk1mgxGTPCQYo6P43ML67RpOHT4Cpwufge5ef8alAuykV/L3n5Aat2l/z0KTr0ITfkOCARCSKVh0Ou1OH5ivc1fipakpp6G7IGOxCZbF43Ssk9QU5sKjpMiNuY5yOXjMW3qTgSZ5w3JZOpuc7osN+r29iro9U0oL//GvHa76Yb9119PorW1DDpdPaprLMP18+y2wXDGcRJMTtiM6OiHB+V4CsVEnD8rFZMmvQk/v7kQCESob/gTGRk34tSpV8CYAWWa7dDrG+DuHgk/377ftEajiIg7IZdPgE5Xg+zsu7A/dQaOn3gU1dW/obb2MGprD+NM6YfQao+C4yRWiUxdCQQCfuQCcGz+qIVEouLnKVpW9eoLMcdhbYTpR+SrpyuwKDMfZW06RMok+P68aKwI8RvwD7H6+mPm4Uc9/P3nI2naNwgOuhoKRRwY06G84usBHb+rk/nPoLh4M45l34kKq2HkDnp+yN7L5nbr4vD7YDTqUG+u59l12kVXY8bcgulJ33Yrc2Z6bkclDUvCVFOzKcNeLPaz2xMmk4VBIOhY/MKRHlI3NyWmJn6OyMh7e93XFk/PKZgxfRfGjFndscqa13RIpWEwGBr5oL+iU2eAQhEHsdgPBkMj6uqO2D1+RcW3MBrbIZdPsLn4R2d8cfwuCUqAqT6sRrMDABAd/Qj/o1IiUUEqDQPAoDWXO9TrG/gRN4UiFkKhhO/NHsryT0Zjm8OrQnWsWNX3xUk4TszXwLYs5jLcUEDaT6tC/CASmFZhyuq01N7plja8ZS71dEuYyqGLtmld9XeQnr4EbW0a82ODc0FubMgD0PNKTZ1ZuvTLyj7F6dNvATBlXibEvwOBwA1VVXtRXv5Vl3Nn/DxEewFpT4lNLS0lyM/fCACIinqQ/7UrFMoQG/MMEs/7DJMTtnY7npubsqPgfnMBThW+DMBU0kmpnAKDoRF5xx9GZdVPYEwHD49xNgtLj2YcJ4a/KgUJ8e/g/FmpCA66FgBDYdGrSM+4ESUlWwAAYWE39btk1WjDcRJMmbwN4WNuM6/iVI/S0o+RmXUT0jOWID1jCf4yzzENC7up196sAP/OAWnv80etn2sZouvfnLG/+3shXi5Di5HBwEzLi/40dRziFAOf92s0tiE3bw0YM8Df/3LETXyFn/doSWDRlG0ftKHSmppUlJb+1/wXQ07ug1ajARZ8UlOndey76hjK3YfGxjwYja0QiTz5wKg/PNyjIBIpYTS2oKDgORiNOqs17O3hODc+8Q6wX/JpKAkEHN+bWabZjra2s3wynr9qHgQCDr7mH7b2hu1raw+hsOg1AKbpSb3dQ+31kJpKsRmhUqV0+8HQdZpEg/leKZUE8z8AnLEQQG7eWhz846Jep/no9Q18HdHOZcP6omNq3i6HV+lzJrrL9FOwVIyF/qYPraXWaHW7HkuyTqFap0eshxRXBfQ+v4MxA/48dhsKCp4DYwYEBCxAVNS/AAz8gny6eDM/dO1I76ClS98ynBIZcR+Cg6+FXD4ekRH3AAD+OvmEVcFtvV7Lz1O1Vxez89rvFowx5B1fB4OhGV6e0/ghnc68vKbC3X1Mt8eBjmH7/Pxn0dpaCok4AOqwlYiNeQ4cJ0FNze/Iz38GgPWNnXQnFvshJmYjJsa+BKHQHXV1h9DaWgo3N+9BKfU0mojFvoiKehCzZv6GKZO3ITDgSri7R8HdPZL/5+PzN4SPubXXY8nlsQgLXYHQ0OXdat/2RuWfAoBDfX0WWlrO9Pl9cAIBXpoQhgu85Xh5QhjeiFFDPsAheotTha+hqekk3Nx8MX7c/1kFHQEBfwfHSdDYdAIN5uLtA6HXNyAvz1RfMyRkKVSqy8BYO/7881Y+CAFMCSOW4VvHisMf5kdfPD0nD+hHm0DAYYx6NQDTMpDpGUtRU2vqDestIDXt0zHtQ2KnTu1QCwq6CoAAdXWHcLr4XQAMSuUUvldY5dcRzHe9tzFmwKnC15CesQzt7VXw8Ih2qLxd53uLJSkN6DpP+4Fuz+uo/2oavrYM18vNFQgAwNc8naGhIZtfdKCrhoYcZGatwtGjV/P/MjJX2Czj1VVzcyEqzKWy8vOftTuVobbuMBgzQCZT2yyB5Qgf75kQi/2gUMQ5vEqfM1FAOgCW7Pmvz9biZFMrbjx2Cqda2hAiccPHCVEOFcKvqUlFVdUecJwYE8Y/hYmxLyMk+HpwnNh8Qe7f3JXy8q/5nsexY9c6NE+I48QIMGeMh4QsRXinJA61+mYoFfHQ6+tx/PjD/MXEMn9UJPICx/W8jnXHr9iOOaS1tQdRW3vQtKZ8zLN9vqBbEptq6/4AAERE3gOhUAYPj0g+UcSSpOCMxKORIDBwIZKmfQO5PAYAoA5bxS9HSfpGIBDCx2cWJk58EckzfkLyjN38vymTtzpUKkwgEGDcuEcxftxjfR4il4j94O1t6lWtqPiuX+8hTuGOzyePxZIg30GbK62tz8Lp0+8AACZMeBJisY/Vdjc3JZ9VbUlsGYiT+RvR2lYGmVSNsVFrMTH2JXh5JcFgaERm1kp+jq1e3xHMiGzUIbXoXBy+uPgDAD0nNPVFePhtiIt7DUKhHFptOh+oOBKQurt3CkgdGLIfKlJpMD/MbRlh6dwZ4O09CwKBGC2txfyiJ4Ap+SkjczkKCzcBMCIo6GpMm7rDoe+IVBoKsVgFo7EVh4/8HVptpnme9nMAep6nzSc21WeCMSMaLQlN5qoagOk7pFQmALBOZLNobi5CRuYKVFf/Am19Bv+vpuZ3ZGbdhMbGE3bPvbhkKyxzf1tai1Fa9lmP+/LD9QNYnITjxJg18zdMTthstwPJVSggHYBJCnfM8pLDwID56X8hvb4Z3iIhPkmIQqCDdUctF9zg4Ov5VVrc3Dw7XZD7nm1aXbMfuXkPAQDCwlZCHfZPh58bPfZhTJ26A+PHPWF1A+I4EWJinwPHiVFd/StfH663kk8WljmkLS1FfIZfcYnpYh4UdHWPvaD2dK4J6e4ehaDAxfzfYWHL+RVr5PIJPSaOkO7c3SMwbeqXSJr2DcY40ItHhi/LD7GCU88jPf0GaDQ7oNf3fZ37wWIwtCE3dw0AIwIC/g7/Hsr5WHrGysu/GVBh8urq31BmvsnHxDwLkcgDQqEE8ZPeNidOnsXRtMWoqTnA9xiJRApwnMjOUTt6SS1Jl/YSmvoiwP9yTE/61mrepL0apBYenQJSR5KahlJw8LVWf3cenROJPODjPQNAxzB4TU0qDh2ej9ragxAK3REb8wJiY551uCQcx7khIWEzZFI1WlvPIC39OuTmPgitNg0cJ+1xnrbcYwI4Tga9vgFNzQUdJZ/ksVb7WYbtC4teQ13dUf7xtvYqZGbeBJ2uBgrFRMRPegvxk95G/KS34emZCL2+AZlZK3usBazT1fH30QD/K0yvUfga9PpGm/vzCU3eA1stz17HkatRQDpAliz6er0RUk6AbfGRGOfRc4mKztrba/iVXboW/rX8XVHxNb/sXG9aWs7gVOFrOHbsdjCmQ4D/FYgeu65PPRtCoRSeygSbz5F7RCMy4j4AwF8n/43W1jK+KL6kl4BUKg2BQOAGo7Edra0aNDXlo7r6ZwACqMNucvj8OnPvFJBGRT1gdRMRCISYGPsS/FXzMHbsun4dfzTjOAkUiolOqSBAhk5Q4CL4mWsM19b9gdy8NdifOgMazZe9PHPwGY3tOPHXBjQ3F0AsVmH8uJ5rX3p7J0MqDYXB0MjX7+wrvb4BecdN3/2w0BXw7rTKlZubJyYnbOGreWRk/gMFp14ybRM5sFqRb+cVuzi+F20wyGRqTE38DOHhdyIo6Jpek6WALkP2LuwhBQCV38X8/H5Pz8RuS936moP5yqo9KCh4ERmZy6HTVUPuMR7Tpn6FoKC+TxFSKuKQlPQN/P0vB2N6fjnNsLAVPQboHCeCUmmqx1pb+wefRKZQWAekISHXQyYLR1tbOdIzlqKo6G3o9Q3IylqFltZiSKVhSIh/HyrVpVCpLoFKdQkS4t+Fh0c02trKkZF5k83h8dLSj2E0tkIuj0Vs7AuQycZAp6vmO2o6a23VmHuUOX7FsJGIAtIBmuurRJxcBqEAeGdiOKZ5Or6ec3nF12BMB4ViYrcvgemCHAK9vsHuBZkxIzTlXyEtfSkOHJyNwsJNMBia4O2djNjY5wY9GUWtXgVPS9JQ3rqODPseapBaCARCfuJ9c0sh/6VT+c3tdxkjpecUABy8vWbw2YOdyWQhmDTpdatl+ggZTYRCGRLi38HM5F8RGXEvZDI1DIZmnPjrCatyN0OtpeUM0tKXQGMeEZow4Sm7meMCAcfX2y0r+7xfr3m6eDPa2sohk6kRFdW91qtUGoRpU3fyyXyVlaakkp5qkHZmKQ4PmFaR61zofjBwnARRkfchNuYZh8r7eHhEQyTyMifk+PS6/1DiOAlCQ28AAIQEX9dtuyWY12rTUXT6TQAMIcFLMHXqjgGNZIlECsRNfBXjxz8JjhNDIgnky7D1xNKzXVb2KRgzwM3Nmy9RaCEW+yFp2tcICFgAxgwoOPU8Ug/MQUNDNtzcfDBl8hZIutz/3Ny8MDnhA0gkgWhuzkfWn6utevqNxjaUnNkGwHRP5Tg3fp5rcfHmbivoWXpHlcr4bosvjCQUkA4QJxBg55SxODwjFil+jhcXZozxBYSDbCyLZnVB7mEeFWMMJ08+hdzcB1BXdwiAAD7esxAb+yImJ3wwJF3zAoEQsbHPm5KGavfzXypxDzVIO7MEntq6oygv3wkAUKsdn07QldwjGrNm/oqEhA+oJ48QO2SyUERE3IXkGXvh7h4Fg6ERZWUDn5/piLOVP+LwkQWor8+ESKRE/KS3oPK7uNfnBQctBiBAbd0fOHHicZs1XXvS1laJEvOP3rFRD/U4D1oolFkl8wH2kzMtOheHH6zh+oEQCmVInvEjkpK+HRYVMSIj7kXyjD0IClrcbZtMFgK5x3gAplWlJk7chAkT/m23+L2jBAIBQkOW4vxZBzE96ftegzdL73Nj43EApuF6W/cSkUiOibEvI2bCRnCcBHp9HThOhoSEzT0mG0qlwZic8AFEIgW02jQcTbsaTU2mebPlFd+ivb0SEkkgP8fW338eFIpJMBiaUFj0htWxamotw/Uz+9Aa5x7Xf3JHAIVIiBBp72WVOmtoyEZj0wlwnLjHpSdNcyIFqK09YDNLtrj4PZSc2QoACA+/05TVO2UbggKv7LWI90C4u0cgKmoNAKC11XRevc0htTwPMPVcGI3tUCri4enAsmf2SKXBEAqH75wYQoYTgYDjp8iUnNnK17wcCkZjG0789X84dux26PX1UCqnIGnad1Cpuo9m2CKVBiMy0jRF6Ezph0hLu8bu8pCdFRa9DoOhGUplgt0lXy0syXzBQdcifMxtDr1GVOQDfdp/qJnqlXq5+jQAmD5n9qpCREevR1DQNUia9jUCAxYM+uu7uXk51BZdk9HkXUYqOxMIBAgOvpbvVZ+c8AE8e5mqIZePR0LC+3Bz80Fj43EcOXolNJodKDEnw4WG/oO/VwsEHMaa76ulpZ/wvaKMGTsVxB/Zo30UkLqIpddTpUrpcdk2mSyEL4B75sw2q+W+NOVfIb/AtKpJ9NiHERV5H6TS4CE+6w5hocvh5dlRqLu3OaRAR2KT0WgaulCrV1HPJiFOFhi4CG5uPmhtLeVXw7EwGNqg0eywKofUH83NRTiadg3O8MOSNyPxvE9sFoW3JyL8Dv6G3tCYg8NHFqLcnH1u77UtK4yNjXrI4WuMu3sEYmI2wstrqkP7y2RhiInZ6NTr7kjh4zMTsTHPDMqqcwMhFvtAJus4h84Z9j2Ry8cjJmaj1Zxke7w8EzE96Xt4e82AwdCM3Lw1aGw6AaHQHSHB11vt6+MzC36+F4ExHTIyl6Pg1MtoaMyFTlcNodDdofnE5zIKSF3AYGhFRYVpadCuyUxdBQWbhu2LS97HgQOzUVDwIjSaL5FnzqJXh61yybKOAgGHmJhnwHGmobCu825skXX6xSyVhkBlXvuYEOI8QqEUoSHLAJiuK5YSbkajHtnZdyI3bw0OH7kChw5fgeKSLd3ms/WmouI7HD6yEA0NOXBz80ZC/GZEj13b62pxPfHznYOkpG/h5TkNBkMjcnLuRd7xR3pM9iw49RIY08PXdzZf9oqQnnh1mnKhkPcekPaHROKPKVO2ISLiHlhWEwwKusZmZ1Rc3GsIDr4OAENR0evIzFxhOk+vpCEd+RwO7Ne2IN0wZsCJExtQpvkSQEePpZubDwICrkBQ4FVQKGLsHqOy8kfo9Q2QSkN6zZgL8L8cjQ15KC37GK1tZeZJ4OZtAQswduzaAb2fgXB3D0dCwnuoqz0Mb3MpD/v7dwSkYaErei2tQggZGqGhN+B08duor8+CVpsGT89EnDjxKKqq90FgXtWtsTEPJ0/+G/n5zyBu4ivw9+/9B2Rx8Qc4mf8UAMDLcxomTny5W5Z1f0glgZgy5SMUFr2KoqI3UVb2Keq1GYiLe80qEaa+/ph5WUQBoiLXDPh1ycin9JwCTfkOCIXuQ9pjKxAIERlxN7y9k1FT8zvGqG+2uZ9QKEXMhKfh7TUDx0+s52tpj/ThegAQsMFam82J6uvr4enpCa1WC6XSeRlnjDH8dfL/cObMh3b3k8tjEBKyFCHB13ebYM4YQ3rGDairO4SIiHsQaWct684MhjZUVe2BpnwHqqt/g6/P+YiPf+ec+sXEGENa2jVo19UiadpXDhU9JoQMjby8dSjTfA6V6lJ4eIxDUdHrADjET3oLXl5TUVHxHco0n6OhIQdSaRhmJu+1m/Gt1zch9cD50OvroVbfjKjIB4fkR2dNTSpycu9He3sVOE6G0JCl4MwJMdXVv6ChIQeBgVdiYuyLg/7aZORpbSvHoUPzoVLNRWzMs64+HSvNzYXIzrkXzc2FmJ70Q5+nvAwHfYnXKCDtg6Kit1Fw6nkAQGzMc/C2rCfLGBoac6HR7DAviWZa/svH+3zETnyRn19pWsJuHc5W/gBAgJnJv0AmC+3zeRgMLeA4ybDIpuwrxhgYM1DvKCEu1th0EocOWfd6Thj/b4SELOH/NhhakHrgb9DpahEX9zoC7CxBXFLyH/x18gnIZGOQPGO3Q+WK+qutrRI5ufeh1sZ69AKBGMkzdvfr2kpGJ8bYsM1nYIzBaGw/Z5N3+xKvUVTgII3mSz4YNWUIWpezkEqDoPK7GDpdLTSaHSg49RJqavfj8OErMDH2JYhEchzLvhutrSUQCEQYF/1Yvy+Y5/JSjgKBAAIBfewIcTW5RzR8fWejuvpXAEBE+N1WwShgutaEhCxFUdEbKCl+v8eAlDEDSkq2AgDUYSuHNBgFAIlEhSmT/4Oysu1obLJentHX528UjJI+Ga7BKGA6t3M1GO0rigwcUFX9C7/ih1p9s92VhdzcvKFWr4KP7wXIzr4LTU0nkZH5DwgEIjCmg1Qairi4V3stF0EIIUMtIuIe1NWlIShocY9LLIaG3IjTp98zrdOtTbdZd7Oycg9aWoshEnkiKOiqoT5tAKY5eSEh1/e+IyHknHDujfk6mcHQgtzcf4ExAwIDrsTYqH859Dy5RzSmTd1pLnrPwJgOKlUKkqZ9S8EoIWRY8FQmYPYFmRg/7rEee4kkEhUCA021kk8Xv29zn+KSzQCA0JClDq9BTgghnVEPaS+EQhkmJ2xGcckWxMRs7NO8TaFQhtiYZ+CvSoHB2AJ/1bxhPTRACBl9HLkmqcNWQqP5ApWVP6GlpRgymZrfptWaek4FAjFCQ/8xlKdKCBnBqIfUAUplPOImvtzvjHY/vwsR4H85BaOEkHOSXD4ePj5/A2BEsXmuqEWxeYnOwIAFkEj8nX9yhJARgQJSQgghvVKHmRbg0Gi2Q1ufhYaGPNTUHsTZs7sAAGHqla48PULIOY6G7AkhhPTKx+d8yD3Go7HpBI4etU5c8vE+Hwr5BBedGSFkJKAeUkIIIb0SCAQYO3YtpNIwiMX+/D+ZLBxRUQ+4+vQIIec46iElhBDiEF/fCzBr5i+uPg1CyAjksh7SN954A+Hh4ZBKpZg+fToOHz7sqlMhhBBCCCEu5JKA9LPPPsP999+PDRs2ID09HQkJCUhJScHZs2ddcTqEEEIIIcSFXLKW/fTp0zFt2jS8/vrrAACj0YiwsDDcddddWLt2ba/Pd+Za9sUFBfh15ydD+hqEEEIIIc4ye9ESqKOihvx1hvVa9u3t7UhLS8O6dev4xziOw9y5c3Hw4EGbz2lra0NbWxv/d319/ZCfp8WvOz/BhVWznfZ6hBBCCCFD6eedn+DGB9e7+jSsOH3IvqqqCgaDAQEBAVaPBwQEoLy83OZzNm7cCE9PT/5fWFiYM06VEEIIIYQ4wTmRZb9u3Trcf//9/N/19fVOC0pnL1qCn2nInhBCCCEjxOxFS1x9Ct04PSD18/ODUChERUWF1eMVFRUIDAy0+RyJRAKJROKM0+tGHRU17Lq1CSGEEEJGEqcP2YvFYiQmJmLv3r38Y0ajEXv37kVycrKzT4cQQgghhLiYS4bs77//fixfvhxTp05FUlISNm3ahKamJtx0002uOB1CCCGEEOJCLglIr7vuOlRWVuKxxx5DeXk5Jk+ejF27dnVLdCKEEEIIISOfS+qQDpQz65ASQgghhJC+60u85rKlQwkhhBBCCAEoICWEEEIIIS5GASkhhBBCCHEpCkgJIYQQQohLUUBKCCGEEEJcigJSQgghhBDiUufEWvZdWSpV1dfXu/hMCCGEEEKILZY4zZEKo+dkQNrQ0AAACAsLc/GZEEIIIYQQexoaGuDp6Wl3n3OyML7RaERZWRkUCgUEAsGQv159fT3CwsJQUlJChfj7iNqu/6jt+o/arv+o7fqH2q3/qO36b7i3HWMMDQ0NCA4OBsfZnyV6TvaQchyH0NBQp7+uUqkclv/h5wJqu/6jtus/arv+o7brH2q3/qO267/h3Ha99YxaUFITIYQQQghxKQpICSGEEEKIS1FA6gCJRIINGzZAIpG4+lTOOdR2/Udt13/Udv1Hbdc/1G79R23XfyOp7c7JpCZCCCGEEDJyUA8pIYQQQghxKQpICSGEEEKIS1FASgghhBBCXIoCUkIIIYQQ4lKjLiB94403EB4eDqlUiunTp+Pw4cP8ttbWVtxxxx3w9fWFXC7H4sWLUVFRYfX84uJizJ8/H+7u7vD398eaNWug1+v57RqNBkuXLsW4cePAcRzuvfdeZ721ITXQdjty5AguvvhieHl5wdvbGykpKcjKyrI6xooVKzBp0iSIRCJceeWVznprQ85e27377ruYM2cOlEolBAIB6urqbB7j+++/x/Tp0yGTyeDt7d2tfe6++24kJiZCIpFg8uTJQ/dmnKyntqupqcFdd92F8ePHQyaTQa1W4+6774ZWq7V5nOrqaoSGhnZr45H6fQXsf+5uueUWREVFQSaTQaVSYeHChTh+/Di/PSsrC0uWLEFYWBhkMhliYmLwyiuvWB1/tLadBWMM8+bNg0AgwFdffWW1bbTeJ4CBt91ovVfYa7c5c+ZAIBBY/bv11lu7HWPr1q2Ij4+HVCqFv78/7rjjDn7budBuoyog/eyzz3D//fdjw4YNSE9PR0JCAlJSUnD27FkAwH333Ydvv/0W27dvx6+//oqysjJcddVV/PMNBgPmz5+P9vZ2HDhwAP/5z3+wdetWPPbYY/w+bW1tUKlUWL9+PRISEpz+HofCQNutsbERl112GdRqNQ4dOoT9+/dDoVAgJSUFOp0OgKltZTIZ7r77bsydO9cl73Mo9NZ2zc3NuOyyy/Dwww/3eIwvv/wSN954I2666SZkZWUhNTUVS5cu7bbfypUrcd111w3Ze3E2e21XVlaGsrIyvPDCC8jOzsbWrVuxa9curFq1yuaxVq1ahfj4+G6Pj8TvK9D75y4xMRFbtmxBXl4efvzxRzDGcOmll8JgMAAA0tLS4O/vj48++gg5OTl45JFHsG7dOrz++uv8a4zWtrPYtGmTzaWrR+t9Ahh4243We4Uj7XbzzTdDo9Hw/5577jmrY7z00kt45JFHsHbtWuTk5GDPnj1ISUnht58T7cZGkaSkJHbHHXfwfxsMBhYcHMw2btzI6urqmJubG9u+fTu/PS8vjwFgBw8eZIwx9r///Y9xHMfKy8v5fd566y2mVCpZW1tbt9ebPXs2u+eee4buDTnJQNvtyJEjDAArLi7m9/nzzz8ZAHby5Mlur7d8+XK2cOHCoXtDTmSv7Tr7+eefGQBWW1tr9bhOp2MhISFs8+bNDr3ehg0bWEJCwkBPe1hwtO0sPv/8cyYWi5lOp7N6/M0332SzZ89me/futdnGFiPl+8pY39suKyuLAWD5+fk9HvP2229nF154oc1to63tMjIyWEhICNNoNAwA27lzJ79ttN4nGBt4243We0Vv7dbbZ6SmpobJZDK2Z88eh15vuLbbqOkhbW9vR1pamtUvA47jMHfuXBw8eBBpaWnQ6XRW2ydMmAC1Wo2DBw8CAA4ePIhJkyYhICCA3yclJQX19fXIyclx3ptxosFot/Hjx8PX1xfvv/8+2tvb0dLSgvfffx8xMTEIDw939ltymt7azhHp6ekoLS0Fx3GYMmUKgoKCMG/ePGRnZw/VaQ8L/Wk7rVYLpVIJkUjEP5abm4snnngC27ZtA8eNjstdX9uuqakJW7ZsQUREBMLCwno8rlarhY+Pz5Cc83DhSNs1Nzdj6dKleOONNxAYGNjtGKPxPgEMTtuNxnuFo9/X//73v/Dz80NcXBzWrVuH5uZmftvu3bthNBpRWlqKmJgYhIaG4tprr0VJSYlT38tAjY4rNICqqioYDAariwQABAQEoLy8HOXl5RCLxfDy8rK5HQDKy8ttPt+ybSQajHZTKBT45Zdf8NFHH0Emk0Eul2PXrl344YcfrIKHkaa3tnPEqVOnAACPP/441q9fj++++w7e3t6YM2cOampqBv2ch4u+tl1VVRWefPJJrF69mn+sra0NS5YswfPPPw+1Wj3k5zxcONp2b775JuRyOeRyOX744Qfs3r0bYrHY5jEPHDiAzz77zKp9RyJH2u6+++7DzJkzsXDhQpvHGI33CWBw2m403iscabelS5fio48+ws8//4x169bhww8/xLJly/h9T506BaPRiKeffhqbNm3CF198gZqaGlxyySVob2936vsZiFETkBLXaWlpwapVqzBr1iz88ccfSE1NRVxcHObPn4+WlhZXn96wZjQaAQCPPPIIFi9ezM/9EwgE2L59u4vPbnior6/H/PnzERsbi8cff5x/fN26dYiJibG6cJMON9xwAzIyMvDrr79i3LhxuPbaa9Ha2tptv+zsbCxcuBAbNmzApZde6oIzHT6++eYb7Nu3D5s2bXL1qZxzHGk7ulfYtnr1aqSkpGDSpEm44YYbsG3bNuzcuRMFBQUATPcJnU6HV199FSkpKZgxYwY++eQTnDx5Ej///LOLz95xoyYg9fPzg1Ao7Jb9XVFRgcDAQAQGBqK9vb1blrNlOwAEBgbafL5l20g0GO328ccfo6ioCFu2bMG0adMwY8YMfPzxxygsLMTXX3/trLfidL21nSOCgoIAALGxsfxjEokEkZGRKC4uHryTHWYcbbuGhgZcdtllUCgU2LlzJ9zc3Pht+/btw/bt2yESiSASiXDxxRfzx96wYYNz3ogLONp2np6eiI6OxgUXXIAvvvgCx48fx86dO62ek5ubi4svvhirV6/G+vXrnXL+rtRb2+3btw8FBQXw8vLiP1cAsHjxYsyZMwfA6LxPAIPTdqPxXtGf+8T06dMBAPn5+QBs3ydUKhX8/PzOqfvEqAlIxWIxEhMTsXfvXv4xo9GIvXv3Ijk5GYmJiXBzc7PafuLECRQXFyM5ORkAkJycjGPHjlllvu3evRtKpdLqgzCSDEa7NTc3g+M4q6xKy9+WHsCRqLe2c4SllNOJEyf4x3Q6HYqKijBmzJhBP+fhwpG2q6+vx6WXXgqxWIxvvvkGUqnU6hhffvklsrKykJmZiczMTGzevBkA8Pvvv1uVQxlp+vO5Y4yBMYa2tjb+sZycHFx44YVYvnw5nnrqqSE/7+Ggt7Zbu3Yt/vzzT/4zlZmZCQB4+eWXsWXLFgCj8z4BDE7bjcZ7RX++r5a2swSis2bNAgCr+0RNTQ2qqqrOrfuEq7OqnOnTTz9lEomEbd26leXm5rLVq1czLy8vPhvy1ltvZWq1mu3bt48dPXqUJScns+TkZP75er2excXFsUsvvZRlZmayXbt2MZVKxdatW2f1OhkZGSwjI4MlJiaypUuXsoyMDJaTk+PU9zqYBtpueXl5TCKRsNtuu43l5uay7OxstmzZMubp6cnKysr4/XJyclhGRgZbsGABmzNnDt+O57Le2k6j0bCMjAz23nvvMQDst99+YxkZGay6upo/xj333MNCQkLYjz/+yI4fP85WrVrF/P39WU1NDb/PyZMnWUZGBrvlllvYuHHj+LazldV7rrDXdlqtlk2fPp1NmjSJ5efnM41Gw//T6/U2j9dTJYOR9n1lzH7bFRQUsKeffpodPXqUnT59mqWmprIFCxYwHx8fVlFRwRhj7NixY0ylUrFly5ZZte3Zs2etXme0tZ0t6JIpPlrvE4wNvO1G673CXrvl5+ezJ554gh09epQVFhayr7/+mkVGRrILLrjA6hgLFy5kEydOZKmpqezYsWPsiiuuYLGxsay9vZ3fZ7i326gKSBlj7LXXXmNqtZqJxWKWlJTE/vjjD35bS0sLu/3225m3tzdzd3dnixYtYhqNxur5RUVFbN68eUwmkzE/Pz/2wAMPdCszA6DbvzFjxjjj7Q2ZgbbbTz/9xGbNmsU8PT2Zt7c3u+iii/iyUBZjxoyx2XbnOnttt2HDBpvvecuWLfw+7e3t7IEHHmD+/v5MoVCwuXPnsuzsbKvXmD17ts3jFBYWOuldDo2e2s4SXPblPfcUkI7E7ytjPbddaWkpmzdvHvP392dubm4sNDSULV26lB0/fpx/bk+fy67tMtrazpauQRVjo/c+wdjA22603it6arfi4mJ2wQUXMB8fHyaRSNjYsWPZmjVrmFartXq+VqtlK1euZF5eXszHx4ctWrTIqnwWY8O/3QSMMTYIHa2EEEIIIYT0y6iZQ0oIIYQQQoYnCkgJIYQQQohLUUBKCCGEEEJcigJSQgghhBDiUhSQEkIIIYQQl6KAlBBCCCGEuBQFpIQQQgghxKUoICWEEEIIIS5FASkhhBBCCHEpCkgJIYQQQohLUUBKCCHDCGMMer3e1adBCCFORQEpIYQMMaPRiI0bNyIiIgIymQwJCQn44osvAAC//PILBAIBfvjhByQmJkIikWD//v0oKCjAwoULERAQALlcjmnTpmHPnj0ufieEEDI0RK4+AUIIGek2btyIjz76CG+//Taio6Px22+/YdmyZVCpVPw+a9euxQsvvIDIyEh4e3ujpKQEl19+OZ566ilIJBJs27YNCxYswIkTJ6BWq134bgghZPAJGGPM1SdBCCEjVVtbG3x8fLBnzx4kJyfzj//zn/9Ec3MzVq9ejQsvvBBfffUVFi5caPdYcXFxuPXWW3HnnXcO9WkTQohTUQ8pIYQMofz8fDQ3N+OSSy6xery9vR1Tpkzh/546darV9sbGRjz++OP4/vvvodFooNfr0dLSguLiYqecNyGEOBMFpIQQMoQaGxsBAN9//z1CQkKstkkkEhQUFAAAPDw8rLY9+OCD2L17N1544QWMHTsWMpkMV199Ndrb251z4oQQ4kQUkBJCyBCKjY2FRCJBcXExZs+e3W27JSDtKjU1FStWrMCiRYsAmALboqKioTxVQghxGQpICSFkCCkUCjz44IO47777YDQacf7550Or1SI1NRVKpRJjxoyx+bzo6Gjs2LEDCxYsgEAgwKOPPgqj0ejksyeEEOeggJQQQobYk08+CZVKhY0bN+LUqVPw8vLCeeedh4cffrjHIPOll17CypUrMXPmTPj5+eGhhx5CfX29k8+cEEKcg7LsCSGEEEKIS1FhfEIIIYQQ4lIUkBJCCCGEEJeigJQQQgghhLgUBaSEEEIIIcSlKCAlhBBCCCEuRQEpIYQQQghxKQpICSGEEEKIS1FASgghhBBCXIoCUkIIIYQQ4lIUkBJCCCGEEJeigJQQQgghhLjU/wPZBIxonV4WJwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# print number of NaNs per era\n",
        "nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n",
        "nans_per_era[target_cols].plot(figsize=(8, 4), title=\"Number of NaNs per Era\", legend=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdPdFvXFxnNc"
      },
      "source": [
        "### Target correlations\n",
        "\n",
        "The targets have a wide range of correlations with each other even though they are all fundamentally related, which should allow the construction of diverse models that ensemble together nicely."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jAvpw-XvxnNc",
        "outputId": "1c0e3f11-e25a-4df6-a0aa-7c040ddfbd33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot correlation matrix of targets\n",
        "import seaborn as sns\n",
        "sns.heatmap(\n",
        "  targets_df[target_cols].corr(),\n",
        "  cmap=\"coolwarm\",\n",
        "  xticklabels=False,\n",
        "  yticklabels=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4DcPlrixnNd"
      },
      "source": [
        "Since we are ultimately trying to predict the main target, it is perhaps most important to consider each auxilliary target's correlation to it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sFN8_azMxnNd",
        "outputId": "4516093b-af13-4ea8-e8c6-efb301a842b6"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \")\",\n  \"rows\": 37,\n  \"fields\": [\n    {\n      \"column\": \"corr_with_cyrus_v4_20\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20790557337103638,\n        \"min\": 0.39173967763293127,\n        \"max\": 1.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.39173967763293127,\n          0.7451322228078557,\n          0.44179771893050096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-1d57e5aa-4852-4898-bc72-1ffb73037e7d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>corr_with_cyrus_v4_20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>target_cyrusd_20</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_xerxes_20</th>\n",
              "      <td>0.942907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_caroline_20</th>\n",
              "      <td>0.922111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_sam_20</th>\n",
              "      <td>0.911981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ralph_20</th>\n",
              "      <td>0.894999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_echo_20</th>\n",
              "      <td>0.851763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_victor_20</th>\n",
              "      <td>0.838408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_waldo_20</th>\n",
              "      <td>0.833260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_delta_20</th>\n",
              "      <td>0.806752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_bravo_20</th>\n",
              "      <td>0.801685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jeremy_20</th>\n",
              "      <td>0.790352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_charlie_20</th>\n",
              "      <td>0.767458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_alpha_20</th>\n",
              "      <td>0.765305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_claudia_20</th>\n",
              "      <td>0.745132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_teager2b_20</th>\n",
              "      <td>0.717080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_agnes_20</th>\n",
              "      <td>0.710171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_tyler_20</th>\n",
              "      <td>0.707080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_rowan_20</th>\n",
              "      <td>0.704642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_cyrusd_60</th>\n",
              "      <td>0.489257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_xerxes_60</th>\n",
              "      <td>0.485867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_caroline_60</th>\n",
              "      <td>0.482128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_sam_60</th>\n",
              "      <td>0.479950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_ralph_60</th>\n",
              "      <td>0.477175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_echo_60</th>\n",
              "      <td>0.461583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_victor_60</th>\n",
              "      <td>0.459265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_waldo_60</th>\n",
              "      <td>0.455948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_delta_60</th>\n",
              "      <td>0.441798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_bravo_60</th>\n",
              "      <td>0.441597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_jeremy_60</th>\n",
              "      <td>0.437988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_charlie_60</th>\n",
              "      <td>0.424056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_alpha_60</th>\n",
              "      <td>0.423646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_claudia_60</th>\n",
              "      <td>0.410047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_teager2b_60</th>\n",
              "      <td>0.398701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_agnes_60</th>\n",
              "      <td>0.396759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_tyler_60</th>\n",
              "      <td>0.396696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target_rowan_60</th>\n",
              "      <td>0.391740</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d57e5aa-4852-4898-bc72-1ffb73037e7d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1d57e5aa-4852-4898-bc72-1ffb73037e7d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1d57e5aa-4852-4898-bc72-1ffb73037e7d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-569127d1-c0ab-4c2e-987f-9b03e3c27b1e\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-569127d1-c0ab-4c2e-987f-9b03e3c27b1e')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-569127d1-c0ab-4c2e-987f-9b03e3c27b1e button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                    corr_with_cyrus_v4_20\n",
              "target_cyrusd_20                 1.000000\n",
              "target                           1.000000\n",
              "target_xerxes_20                 0.942907\n",
              "target_caroline_20               0.922111\n",
              "target_sam_20                    0.911981\n",
              "target_ralph_20                  0.894999\n",
              "target_echo_20                   0.851763\n",
              "target_victor_20                 0.838408\n",
              "target_waldo_20                  0.833260\n",
              "target_delta_20                  0.806752\n",
              "target_bravo_20                  0.801685\n",
              "target_jeremy_20                 0.790352\n",
              "target_charlie_20                0.767458\n",
              "target_alpha_20                  0.765305\n",
              "target_claudia_20                0.745132\n",
              "target_teager2b_20               0.717080\n",
              "target_agnes_20                  0.710171\n",
              "target_tyler_20                  0.707080\n",
              "target_rowan_20                  0.704642\n",
              "target_cyrusd_60                 0.489257\n",
              "target_xerxes_60                 0.485867\n",
              "target_caroline_60               0.482128\n",
              "target_sam_60                    0.479950\n",
              "target_ralph_60                  0.477175\n",
              "target_echo_60                   0.461583\n",
              "target_victor_60                 0.459265\n",
              "target_waldo_60                  0.455948\n",
              "target_delta_60                  0.441798\n",
              "target_bravo_60                  0.441597\n",
              "target_jeremy_60                 0.437988\n",
              "target_charlie_60                0.424056\n",
              "target_alpha_60                  0.423646\n",
              "target_claudia_60                0.410047\n",
              "target_teager2b_60               0.398701\n",
              "target_agnes_60                  0.396759\n",
              "target_tyler_60                  0.396696\n",
              "target_rowan_60                  0.391740"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(\n",
        "    targets_df[target_cols]\n",
        "    .corrwith(targets_df[MAIN_TARGET])\n",
        "    .sort_values(ascending=False)\n",
        "    .to_frame(\"corr_with_cyrus_v4_20\")\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jl7_XIKfxnNd"
      },
      "source": [
        "## 2. Target Selection\n",
        "\n",
        "Our goal is to create an ensemble of models trained on different targets. But which targets should we use?\n",
        "\n",
        "When deciding which model to ensemble, we should consider a few things:\n",
        "\n",
        "- The performance of the predictions of the model trained on the target vs the main target\n",
        "\n",
        "- The correlation between the target and the main target\n",
        "\n",
        "To keep things simple and fast, let's just arbitrarily pick a few 20-day targets to evaluate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YDRiSoPxnNd"
      },
      "source": [
        "### Model training and generating validation predictions\n",
        "\n",
        "Like usual we train on the training dataset, but this time we do it for each target.\n",
        "\n",
        "Sit back and relax, this will take a while\n",
        "# ☕"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VqBaYwmqxnNd",
        "outputId": "6716cbd7-827c-4593-b65d-25d120de0af5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002188 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 210\n",
            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
            "[LightGBM] [Info] Start training from score 0.500008\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004084 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 210\n",
            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
            "[LightGBM] [Info] Start training from score 0.500003\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002349 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 210\n",
            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
            "[LightGBM] [Info] Start training from score 0.500031\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.083061 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 210\n",
            "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n",
            "[LightGBM] [Info] Start training from score 0.499948\n"
          ]
        }
      ],
      "source": [
        "import lightgbm as lgb\n",
        "\n",
        "models = {}\n",
        "for target in TARGET_CANDIDATES:\n",
        "    model = lgb.LGBMRegressor(\n",
        "        n_estimators=2000,\n",
        "        learning_rate=0.01,\n",
        "        max_depth=5,\n",
        "        num_leaves=2**4-1,\n",
        "        colsample_bytree=0.1\n",
        "    )\n",
        "    # We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n",
        "    # model = lgb.LGBMRegressor(\n",
        "    #     n_estimators=30_000,\n",
        "    #     learning_rate=0.001,\n",
        "    #     max_depth=10,\n",
        "    #     num_leaves=2**10,\n",
        "    #     colsample_bytree=0.1\n",
        "    #     min_data_in_leaf=10000,\n",
        "    # )\n",
        "    model.fit(\n",
        "        train[feature_cols],\n",
        "        train[target]\n",
        "    )\n",
        "    models[target] = model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jD6j1JBxnNe"
      },
      "source": [
        "Then we will generate predictions on the validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "Ic9eGKxSxnNe",
        "outputId": "e3522459-1f43-4d45-d20c-a6edfcf6f28a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v5.1/validation.parquet: 3.45GB [01:27, 39.5MB/s]                            \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-62468f07-723f-4ee4-87da-205c05aa1bfa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <th>prediction_target_xerxes_20</th>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n000c290e4364875</th>\n",
              "      <td>0.495167</td>\n",
              "      <td>0.491972</td>\n",
              "      <td>0.495561</td>\n",
              "      <td>0.496844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002a15bc5575bbb</th>\n",
              "      <td>0.516067</td>\n",
              "      <td>0.512950</td>\n",
              "      <td>0.515098</td>\n",
              "      <td>0.508923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n00309caaa0f955e</th>\n",
              "      <td>0.513778</td>\n",
              "      <td>0.512101</td>\n",
              "      <td>0.513682</td>\n",
              "      <td>0.505769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0039cbdcf835708</th>\n",
              "      <td>0.507834</td>\n",
              "      <td>0.505156</td>\n",
              "      <td>0.506836</td>\n",
              "      <td>0.504405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n004143458984f89</th>\n",
              "      <td>0.484917</td>\n",
              "      <td>0.485125</td>\n",
              "      <td>0.486912</td>\n",
              "      <td>0.490126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffc5b7319b4b998</th>\n",
              "      <td>0.497589</td>\n",
              "      <td>0.491416</td>\n",
              "      <td>0.497194</td>\n",
              "      <td>0.498360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffd7ad35b86d121</th>\n",
              "      <td>0.509668</td>\n",
              "      <td>0.504195</td>\n",
              "      <td>0.508191</td>\n",
              "      <td>0.501186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffdb1a3a768a420</th>\n",
              "      <td>0.498573</td>\n",
              "      <td>0.502095</td>\n",
              "      <td>0.496591</td>\n",
              "      <td>0.502693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffdc129924fae18</th>\n",
              "      <td>0.493419</td>\n",
              "      <td>0.489640</td>\n",
              "      <td>0.493709</td>\n",
              "      <td>0.498717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nfff193e9bccc4f1</th>\n",
              "      <td>0.494057</td>\n",
              "      <td>0.494121</td>\n",
              "      <td>0.493246</td>\n",
              "      <td>0.495440</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>916263 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62468f07-723f-4ee4-87da-205c05aa1bfa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-62468f07-723f-4ee4-87da-205c05aa1bfa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-62468f07-723f-4ee4-87da-205c05aa1bfa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-6b30a6e8-8851-42af-b7c2-d88d0c1b256e\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b30a6e8-8851-42af-b7c2-d88d0c1b256e')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-6b30a6e8-8851-42af-b7c2-d88d0c1b256e button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
              "id                                                                           \n",
              "n000c290e4364875                     0.495167                     0.491972   \n",
              "n002a15bc5575bbb                     0.516067                     0.512950   \n",
              "n00309caaa0f955e                     0.513778                     0.512101   \n",
              "n0039cbdcf835708                     0.507834                     0.505156   \n",
              "n004143458984f89                     0.484917                     0.485125   \n",
              "...                                       ...                          ...   \n",
              "nffc5b7319b4b998                     0.497589                     0.491416   \n",
              "nffd7ad35b86d121                     0.509668                     0.504195   \n",
              "nffdb1a3a768a420                     0.498573                     0.502095   \n",
              "nffdc129924fae18                     0.493419                     0.489640   \n",
              "nfff193e9bccc4f1                     0.494057                     0.494121   \n",
              "\n",
              "                  prediction_target_xerxes_20  prediction_target_teager2b_20  \n",
              "id                                                                            \n",
              "n000c290e4364875                     0.495561                       0.496844  \n",
              "n002a15bc5575bbb                     0.515098                       0.508923  \n",
              "n00309caaa0f955e                     0.513682                       0.505769  \n",
              "n0039cbdcf835708                     0.506836                       0.504405  \n",
              "n004143458984f89                     0.486912                       0.490126  \n",
              "...                                       ...                            ...  \n",
              "nffc5b7319b4b998                     0.497194                       0.498360  \n",
              "nffd7ad35b86d121                     0.508191                       0.501186  \n",
              "nffdb1a3a768a420                     0.496591                       0.502693  \n",
              "nffdc129924fae18                     0.493709                       0.498717  \n",
              "nfff193e9bccc4f1                     0.493246                       0.495440  \n",
              "\n",
              "[916263 rows x 4 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Download validation data\n",
        "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n",
        "\n",
        "# Load the validation data, filtering for data_type == \"validation\"\n",
        "validation = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/validation.parquet\",\n",
        "    columns=[\"era\", \"data_type\"] + feature_cols + target_cols\n",
        ")\n",
        "validation = validation[validation[\"data_type\"] == \"validation\"]\n",
        "del validation[\"data_type\"]\n",
        "\n",
        "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n",
        "# Comment out the line below to use all the data\n",
        "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n",
        "\n",
        "# Embargo overlapping eras from training data\n",
        "last_train_era = int(train[\"era\"].unique()[-1])\n",
        "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n",
        "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n",
        "\n",
        "# Generate validation predictions for each model\n",
        "for target in TARGET_CANDIDATES:\n",
        "    validation[f\"prediction_{target}\"] = models[target].predict(validation[feature_cols])\n",
        "\n",
        "pred_cols = [f\"prediction_{target}\" for target in TARGET_CANDIDATES]\n",
        "validation[pred_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ea2Z98CIxnNe"
      },
      "source": [
        "### Evaluating the performance of each model\n",
        "\n",
        "Now we can evaluate the performance of our models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8aLpCC3xnNf"
      },
      "source": [
        "As you can see in the performance chart below, models trained on the auxiliary target are able to predict the main target pretty well, but the model trained on the main target performs the best."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "WUvsFi-VxnNf",
        "outputId": "39a65698-cf42-4a58-fe75-eaca8ec2d224"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipython-input-13-1867405524.py:5: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  correlations = validation.groupby(\"era\").apply(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# import the 2 scoring functions\n",
        "from numerai_tools.scoring import numerai_corr, correlation_contribution\n",
        "\n",
        "prediction_cols = [\n",
        "    f\"prediction_{target}\"\n",
        "    for target in TARGET_CANDIDATES\n",
        "]\n",
        "correlations = validation.groupby(\"era\").apply(\n",
        "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
        ")\n",
        "cumsum_corrs = correlations.cumsum()\n",
        "cumsum_corrs.plot(\n",
        "  title=\"Cumulative Correlation of validation Predictions\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDUjJ3guxnNf"
      },
      "source": [
        "Looking at the summary metrics below:\n",
        "- the models trained on `victor` and `xerxes` have the highest means, but `victor` is less correlated with `cyrus` than `xerxes` is, which means `victor` could be better in ensembling\n",
        "- the model trained on `teager` has the lowest mean, but `teager` is significantly less correlated with `cyrus` than any other target shown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "smz_GLLAxnNf",
        "outputId": "75d86754-9e57-492a-c776-748dd04ca32f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
            "/tmp/ipython-input-14-2473492708.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0013552110339609415,\n        \"min\": 0.014269053347590915,\n        \"max\": 0.017251514021665453,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.01634063554282721,\n          0.014269053347590915,\n          0.017010595159348097\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0007367392693322167,\n        \"min\": 0.01706845463891172,\n        \"max\": 0.01863158016168851,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.018440140254275643,\n          0.01706845463891172,\n          0.01863158016168851\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04137802965784783,\n        \"min\": 0.8359897629549377,\n        \"max\": 0.9310500321598153,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8861448621052853,\n          0.8359897629549377,\n          0.9129979857707619\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00608894516944781,\n        \"min\": 0.03903809673347092,\n        \"max\": 0.052751291342750584,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.03903809673347092,\n          0.052751291342750584,\n          0.04091138900274505\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_corr_with_cryus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0012024478703817265,\n        \"min\": 0.015044443122822021,\n        \"max\": 0.017690434011823932,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.016537659386694534,\n          0.015044443122822021,\n          0.017468332085298733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "summary"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-63115654-e64c-42b8-8e09-cf0a15f503d4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
              "      <th>mean_corr_with_cryus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <td>0.017011</td>\n",
              "      <td>0.018632</td>\n",
              "      <td>0.912998</td>\n",
              "      <td>0.040911</td>\n",
              "      <td>0.017468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <td>0.016341</td>\n",
              "      <td>0.018440</td>\n",
              "      <td>0.886145</td>\n",
              "      <td>0.039038</td>\n",
              "      <td>0.016538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_xerxes_20</th>\n",
              "      <td>0.017252</td>\n",
              "      <td>0.018529</td>\n",
              "      <td>0.931050</td>\n",
              "      <td>0.043307</td>\n",
              "      <td>0.017690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <td>0.014269</td>\n",
              "      <td>0.017068</td>\n",
              "      <td>0.835990</td>\n",
              "      <td>0.052751</td>\n",
              "      <td>0.015044</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63115654-e64c-42b8-8e09-cf0a15f503d4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-63115654-e64c-42b8-8e09-cf0a15f503d4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-63115654-e64c-42b8-8e09-cf0a15f503d4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-bcf11ce9-b717-46fd-9d8b-03bba5d67b47\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bcf11ce9-b717-46fd-9d8b-03bba5d67b47')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-bcf11ce9-b717-46fd-9d8b-03bba5d67b47 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_2ec38695-2eca-4439-8834-ec5b874ae511\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2ec38695-2eca-4439-8834-ec5b874ae511 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown  \\\n",
              "prediction_target_cyrusd_20   0.017011 0.018632 0.912998      0.040911   \n",
              "prediction_target_victor_20   0.016341 0.018440 0.886145      0.039038   \n",
              "prediction_target_xerxes_20   0.017252 0.018529 0.931050      0.043307   \n",
              "prediction_target_teager2b_20 0.014269 0.017068 0.835990      0.052751   \n",
              "\n",
              "                               mean_corr_with_cryus  \n",
              "prediction_target_cyrusd_20                0.017468  \n",
              "prediction_target_victor_20                0.016538  \n",
              "prediction_target_xerxes_20                0.017690  \n",
              "prediction_target_teager2b_20              0.015044  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_summary_metrics(scores, cumsum_scores):\n",
        "    summary_metrics = {}\n",
        "    # per era correlation between predictions of the model trained on this target and cyrus\n",
        "    mean = scores.mean()\n",
        "    std = scores.std()\n",
        "    sharpe = mean / std\n",
        "    rolling_max = cumsum_scores.expanding(min_periods=1).max()\n",
        "    max_drawdown = (rolling_max - cumsum_scores).max()\n",
        "    return {\n",
        "        \"mean\": mean,\n",
        "        \"std\": std,\n",
        "        \"sharpe\": sharpe,\n",
        "        \"max_drawdown\": max_drawdown,\n",
        "    }\n",
        "\n",
        "target_summary_metrics = {}\n",
        "for pred_col in prediction_cols:\n",
        "  target_summary_metrics[pred_col] = get_summary_metrics(\n",
        "      correlations[pred_col], cumsum_corrs[pred_col]\n",
        "  )\n",
        "  # per era correlation between this target and cyrus\n",
        "  mean_corr_with_cryus = validation.groupby(\"era\").apply(\n",
        "      lambda d: d[pred_col].corr(d[MAIN_TARGET])\n",
        "  ).mean()\n",
        "  target_summary_metrics[pred_col].update({\n",
        "      \"mean_corr_with_cryus\": mean_corr_with_cryus\n",
        "  })\n",
        "\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary = pd.DataFrame(target_summary_metrics).T\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yju_5cHJxnNf"
      },
      "source": [
        "### Selecting our favorite target\n",
        "Based on our observations above, it seems like target `victor` is the best candidate target for our ensemble since it has great performance and it is not too correlated with `cyrus`. However, it's interesting to look at how models that are very uncorrelated ensemble together we are going to also look at how `teager` ensembles with `cyrus`.\n",
        "\n",
        "What do you think?\n",
        "\n",
        "Note that this target selection heuristic is extremely basic. In your own research, you will most likely want to consider all targets instead of just our favorites, and may want to experiment with different ways of selecting your ensemble targets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeqgd58hxnNg"
      },
      "source": [
        "## 3. Ensembling\n",
        "\n",
        "Now that we have reviewed and selected our favorite targets, let's ensemble our predictions and re-evaluate performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rap7tHjGxnNg"
      },
      "source": [
        "### Creating the ensemble\n",
        "\n",
        "For simplicity, we will equal weight the predictions from target `victor` and `cyrus`. Note that this is an extremely basic and arbitrary way of selecting ensemble weights. In your research, you may want to experiment with different ways of setting ensemble weights.\n",
        "\n",
        "Tip: remember to always normalize (percentile rank) your predictions before averaging so that they are comparable!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "y27jxEUwxnNg",
        "outputId": "17962176-35ec-4d5a-e891-1b51779b961a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-8a0b7416-00c1-4ced-9eed-5cde61884c7a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <th>ensemble_cyrus_teager</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n000c290e4364875</th>\n",
              "      <td>0.495167</td>\n",
              "      <td>0.491972</td>\n",
              "      <td>0.496844</td>\n",
              "      <td>0.183879</td>\n",
              "      <td>0.253067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002a15bc5575bbb</th>\n",
              "      <td>0.516067</td>\n",
              "      <td>0.512950</td>\n",
              "      <td>0.508923</td>\n",
              "      <td>0.965491</td>\n",
              "      <td>0.968643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n00309caaa0f955e</th>\n",
              "      <td>0.513778</td>\n",
              "      <td>0.512101</td>\n",
              "      <td>0.505769</td>\n",
              "      <td>0.952369</td>\n",
              "      <td>0.917604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0039cbdcf835708</th>\n",
              "      <td>0.507834</td>\n",
              "      <td>0.505156</td>\n",
              "      <td>0.504405</td>\n",
              "      <td>0.807941</td>\n",
              "      <td>0.831118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n004143458984f89</th>\n",
              "      <td>0.484917</td>\n",
              "      <td>0.485125</td>\n",
              "      <td>0.490126</td>\n",
              "      <td>0.012526</td>\n",
              "      <td>0.020024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffc5b7319b4b998</th>\n",
              "      <td>0.497589</td>\n",
              "      <td>0.491416</td>\n",
              "      <td>0.498360</td>\n",
              "      <td>0.247458</td>\n",
              "      <td>0.380044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffd7ad35b86d121</th>\n",
              "      <td>0.509668</td>\n",
              "      <td>0.504195</td>\n",
              "      <td>0.501186</td>\n",
              "      <td>0.814744</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffdb1a3a768a420</th>\n",
              "      <td>0.498573</td>\n",
              "      <td>0.502095</td>\n",
              "      <td>0.502693</td>\n",
              "      <td>0.533047</td>\n",
              "      <td>0.571656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffdc129924fae18</th>\n",
              "      <td>0.493419</td>\n",
              "      <td>0.489640</td>\n",
              "      <td>0.498717</td>\n",
              "      <td>0.120829</td>\n",
              "      <td>0.291945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nfff193e9bccc4f1</th>\n",
              "      <td>0.494057</td>\n",
              "      <td>0.494121</td>\n",
              "      <td>0.495440</td>\n",
              "      <td>0.207340</td>\n",
              "      <td>0.195742</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>916263 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a0b7416-00c1-4ced-9eed-5cde61884c7a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8a0b7416-00c1-4ced-9eed-5cde61884c7a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8a0b7416-00c1-4ced-9eed-5cde61884c7a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-8f751442-7038-499a-9695-1cb8757475e6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f751442-7038-499a-9695-1cb8757475e6')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-8f751442-7038-499a-9695-1cb8757475e6 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                  prediction_target_cyrusd_20  prediction_target_victor_20  \\\n",
              "id                                                                           \n",
              "n000c290e4364875                     0.495167                     0.491972   \n",
              "n002a15bc5575bbb                     0.516067                     0.512950   \n",
              "n00309caaa0f955e                     0.513778                     0.512101   \n",
              "n0039cbdcf835708                     0.507834                     0.505156   \n",
              "n004143458984f89                     0.484917                     0.485125   \n",
              "...                                       ...                          ...   \n",
              "nffc5b7319b4b998                     0.497589                     0.491416   \n",
              "nffd7ad35b86d121                     0.509668                     0.504195   \n",
              "nffdb1a3a768a420                     0.498573                     0.502095   \n",
              "nffdc129924fae18                     0.493419                     0.489640   \n",
              "nfff193e9bccc4f1                     0.494057                     0.494121   \n",
              "\n",
              "                  prediction_target_teager2b_20  ensemble_cyrus_victor  \\\n",
              "id                                                                       \n",
              "n000c290e4364875                       0.496844               0.183879   \n",
              "n002a15bc5575bbb                       0.508923               0.965491   \n",
              "n00309caaa0f955e                       0.505769               0.952369   \n",
              "n0039cbdcf835708                       0.504405               0.807941   \n",
              "n004143458984f89                       0.490126               0.012526   \n",
              "...                                         ...                    ...   \n",
              "nffc5b7319b4b998                       0.498360               0.247458   \n",
              "nffd7ad35b86d121                       0.501186               0.814744   \n",
              "nffdb1a3a768a420                       0.502693               0.533047   \n",
              "nffdc129924fae18                       0.498717               0.120829   \n",
              "nfff193e9bccc4f1                       0.495440               0.207340   \n",
              "\n",
              "                  ensemble_cyrus_teager  \n",
              "id                                       \n",
              "n000c290e4364875               0.253067  \n",
              "n002a15bc5575bbb               0.968643  \n",
              "n00309caaa0f955e               0.917604  \n",
              "n0039cbdcf835708               0.831118  \n",
              "n004143458984f89               0.020024  \n",
              "...                                 ...  \n",
              "nffc5b7319b4b998               0.380044  \n",
              "nffd7ad35b86d121               0.750000  \n",
              "nffdb1a3a768a420               0.571656  \n",
              "nffdc129924fae18               0.291945  \n",
              "nfff193e9bccc4f1               0.195742  \n",
              "\n",
              "[916263 rows x 5 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Ensemble predictions together with a simple average\n",
        "validation[\"ensemble_cyrus_victor\"] = (\n",
        "    validation\n",
        "    .groupby(\"era\")[[\n",
        "      f\"prediction_{MAIN_TARGET}\",\n",
        "      \"prediction_target_victor_20\",\n",
        "    ]]\n",
        "    .rank(pct=True)\n",
        "    .mean(axis=1)\n",
        ")\n",
        "validation[\"ensemble_cyrus_teager\"] = (\n",
        "    validation\n",
        "    .groupby(\"era\")[[\n",
        "      f\"prediction_{MAIN_TARGET}\",\n",
        "      \"prediction_target_teager2b_20\",\n",
        "    ]]\n",
        "    .rank(pct=True)\n",
        "    .mean(axis=1)\n",
        ")\n",
        "\n",
        "# Print the ensemble predictions\n",
        "prediction_cols = [\n",
        "  \"prediction_target_cyrusd_20\",\n",
        "  \"prediction_target_victor_20\",\n",
        "  \"prediction_target_teager2b_20\",\n",
        "  \"ensemble_cyrus_victor\",\n",
        "  \"ensemble_cyrus_teager\"\n",
        "]\n",
        "validation[prediction_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMLgtnXTxnNg"
      },
      "source": [
        "### Evaluating performance of the ensemble\n",
        "Looking at the performance chart below, we can see that the peformance of our ensembles are better than that of the models trained on individual targets. Is this a result you would have expected or does it surprise you?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "jCRbKxZmxnNg",
        "outputId": "313c0821-f81d-46fe-f54c-e5ce95a583f7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipython-input-16-2516148216.py:1: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  correlations = validation.groupby(\"era\").apply(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Cumulative Correlation of validation Predictions'}, xlabel='era'>"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "correlations = validation.groupby(\"era\").apply(\n",
        "    lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n",
        ")\n",
        "cumsum_corrs = correlations.cumsum()\n",
        "\n",
        "cumsum_corrs = pd.DataFrame(cumsum_corrs)\n",
        "cumsum_corrs.plot(\n",
        "    title=\"Cumulative Correlation of validation Predictions\",\n",
        "    figsize=(10, 6),\n",
        "    xticks=[]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FjFZq-hxnNh"
      },
      "source": [
        "Looking at the summary metrics below, we can see that our ensemble seems to have better `mean`, `sharpe`, and `max_drawdown` than our original model. Much more interestingly, however, is that our ensemble with `teager` has even higher sharpe than the ensemble with `victor`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_79JgZULxnNh",
        "outputId": "b843f522-90c8-48a2-af55-a5b11cc19555"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0011089376634754338,\n        \"min\": 0.014269053347590913,\n        \"max\": 0.017010595159348104,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01634063554282721,\n          0.016208824024693678,\n          0.014269053347590913\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0006737207554049439,\n        \"min\": 0.01706845463891172,\n        \"max\": 0.018702182308678757,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.018440140254275643,\n          0.017988282096915168,\n          0.01706845463891172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.030790512224679364,\n        \"min\": 0.8359897629549377,\n        \"max\": 0.9129979857707623,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8861448621052853,\n          0.9010768197521957,\n          0.8359897629549377\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005681096501031452,\n        \"min\": 0.03903809673347092,\n        \"max\": 0.052751291342750584,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.03903809673347092,\n          0.04640399457982047,\n          0.052751291342750584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "summary"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-28906ba3-4fa4-461b-a9b7-69aac3d713e6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <td>0.017011</td>\n",
              "      <td>0.018632</td>\n",
              "      <td>0.912998</td>\n",
              "      <td>0.040911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <td>0.016341</td>\n",
              "      <td>0.018440</td>\n",
              "      <td>0.886145</td>\n",
              "      <td>0.039038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <td>0.014269</td>\n",
              "      <td>0.017068</td>\n",
              "      <td>0.835990</td>\n",
              "      <td>0.052751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <td>0.016925</td>\n",
              "      <td>0.018702</td>\n",
              "      <td>0.904973</td>\n",
              "      <td>0.040448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_teager</th>\n",
              "      <td>0.016209</td>\n",
              "      <td>0.017988</td>\n",
              "      <td>0.901077</td>\n",
              "      <td>0.046404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28906ba3-4fa4-461b-a9b7-69aac3d713e6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-28906ba3-4fa4-461b-a9b7-69aac3d713e6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-28906ba3-4fa4-461b-a9b7-69aac3d713e6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-ed1bb218-19d8-4c12-8277-0d00f59ed43d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed1bb218-19d8-4c12-8277-0d00f59ed43d')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-ed1bb218-19d8-4c12-8277-0d00f59ed43d button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_7202e2c2-6457-4122-a03f-7cace40b4378\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7202e2c2-6457-4122-a03f-7cace40b4378 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20   0.017011 0.018632 0.912998      0.040911\n",
              "prediction_target_victor_20   0.016341 0.018440 0.886145      0.039038\n",
              "prediction_target_teager2b_20 0.014269 0.017068 0.835990      0.052751\n",
              "ensemble_cyrus_victor         0.016925 0.018702 0.904973      0.040448\n",
              "ensemble_cyrus_teager         0.016209 0.017988 0.901077      0.046404"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "summary_metrics = get_summary_metrics(correlations, cumsum_corrs)\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary = pd.DataFrame(summary_metrics)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kns8BQ1kL2mE"
      },
      "source": [
        "You can see below that ensembling also improves MMC performance significantly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "k9jKnsnuL1se",
        "outputId": "85142579-bb14-4847-e6ba-108aa6caf235"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v4.3/meta_model.parquet: 29.0MB [00:00, 36.3MB/s]                            \n",
            "/tmp/ipython-input-18-2344724651.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005403236654931237,\n        \"min\": 0.006538985500454161,\n        \"max\": 0.007755686441149832,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.006538985500454161,\n          0.007615234289732852,\n          0.007755686441149832\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005376619402493899,\n        \"min\": 0.01665597857523989,\n        \"max\": 0.017877044515714754,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.017877044515714754,\n          0.016783463842404655,\n          0.01665597857523989\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04301257695323672,\n        \"min\": 0.36577553379732813,\n        \"max\": 0.46563979451073056,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.36577553379732813,\n          0.453734363849994,\n          0.46563979451073056\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006699420402922103,\n        \"min\": 0.06387804840326167,\n        \"max\": 0.08213650174076936,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07671380497621222,\n          0.07495501510345892,\n          0.08213650174076936\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "summary"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-e45df66a-389f-4c5f-9cd6-ac6813df5d02\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <td>0.006842</td>\n",
              "      <td>0.016903</td>\n",
              "      <td>0.404755</td>\n",
              "      <td>0.063878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <td>0.006539</td>\n",
              "      <td>0.017877</td>\n",
              "      <td>0.365776</td>\n",
              "      <td>0.076714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <td>0.007756</td>\n",
              "      <td>0.016656</td>\n",
              "      <td>0.465640</td>\n",
              "      <td>0.082137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <td>0.006798</td>\n",
              "      <td>0.017585</td>\n",
              "      <td>0.386574</td>\n",
              "      <td>0.072389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_teager</th>\n",
              "      <td>0.007615</td>\n",
              "      <td>0.016783</td>\n",
              "      <td>0.453734</td>\n",
              "      <td>0.074955</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e45df66a-389f-4c5f-9cd6-ac6813df5d02')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e45df66a-389f-4c5f-9cd6-ac6813df5d02 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e45df66a-389f-4c5f-9cd6-ac6813df5d02');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-c5067848-c347-40d5-a4a7-b82e0e8a0ddb\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5067848-c347-40d5-a4a7-b82e0e8a0ddb')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-c5067848-c347-40d5-a4a7-b82e0e8a0ddb button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_e2453f9e-2c4a-47aa-a6bb-2370be6fa934\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e2453f9e-2c4a-47aa-a6bb-2370be6fa934 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20   0.006842 0.016903 0.404755      0.063878\n",
              "prediction_target_victor_20   0.006539 0.017877 0.365776      0.076714\n",
              "prediction_target_teager2b_20 0.007756 0.016656 0.465640      0.082137\n",
              "ensemble_cyrus_victor         0.006798 0.017585 0.386574      0.072389\n",
              "ensemble_cyrus_teager         0.007615 0.016783 0.453734      0.074955"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAILCAYAAAAg4OkIAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdYFMf/wPH30XsHEVS6gNLsLYq9G7HEGksiaoz1m5iYYu+xmxg1iRErGms0MfYeVKwoNlDEhiIoCNLLze8Pfl5EQIoolnk9D4/e7uzM7N5y7OemKYQQAkmSJEmSJEmSpHeIWllXQJIkSZIkSZIkqbTJQEeSJEmSJEmSpHeODHQkSZIkSZIkSXrnyEBHkiRJkiRJkqR3jgx0JEmSJEmSJEl658hAR5IkSZIkSZKkd44MdCRJkiRJkiRJeufIQEeSJEmSJEmSpHeODHQkSZIkSZIkSXrnyEBHkqS3Rv/+/bG3ty/VPFesWIFCoeDmzZulmq8Es2fPxtHREXV1dXx8fMq6OmXK3t6e/v37q14fOnQIhULBoUOHXms9GjduTOPGjV9rmSWR3+9ladd94sSJKBSKUstPkqQ3jwx0JOk9ExERweDBg3F0dERHRwcjIyMaNGjAwoULSU1NLevqvTLTp0/nzz//LOtqqDx9kFMoFPz777959gshqFixIgqFgvbt2+fa9/Q4f3//fPP+/vvvVWkePnyYZ/+hQ4fo3Lkz1tbWaGlpYWVlRYcOHdiyZUvpnBywZ88evv76axo0aEBAQADTp08vMG3//v1RKBR4eXkhhMizX6FQMGzYsFKrW36OHTvGxIkTefz48Sst521hb2+vuocUCgVWVlY0bNiQrVu3lnXViiUlJYWJEye+9oBSkqQ3gwx0JOk9smPHDjw9PdmwYQMdOnTgp59+YsaMGVSqVImvvvqKkSNHlnUVX5mCAp0+ffqQmpqKnZ3d668UoKOjQ2BgYJ7thw8f5u7du2hraxd43ObNm8nIyMizb926dejo6OR73IQJE2jSpAkXL15k8ODBLF26lK+++oqkpCS6dOmSb11K4sCBA6ipqfH777/Tt29f2rZtW+gxoaGhpRpsFcexY8eYNGnSawt0GjVqRGpqKo0aNXot5ZWEj48Pq1evZvXq1YwePZp79+7RuXNnli5dWib12bNnD3v27CnWMSkpKUyaNCnfQGfs2LHv9Jc7kiSBRllXQJKk1yMyMpIePXpgZ2fHgQMHKF++vGrf0KFDuX79Ojt27CjDGpYNdXV11NXVy6z8tm3bsnHjRn788Uc0NP77SA4MDKRGjRr5tsgAtG7dmu3bt7Nz5046duyo2n7s2DEiIyPp0qULmzdvznXMpk2bmDx5Ml27diUwMBBNTU3Vvq+++ordu3eTmZlZKucVExODrq4uWlpaRUqvq6tLxYoVmTx5Mp07d36juxQplUoyMjIKDCaLQk1N7aWOfx1sbW35+OOPVa/79u2Ls7Mz8+fP57PPPsv3mKysLJRKZZHf9+Io7Tw1NDRy/c5JkvTukS06kvSemDVrFklJSfz++++5gpynnJ2dVS06N2/eRKFQsGLFijzpFAoFEydOVL1+2s89PDycjz/+GGNjYywtLRk3bhxCCO7cuUPHjh0xMjLC2tqauXPn5sqvoDEyRR3DMGfOHOrXr4+5uTm6urrUqFGDTZs25alzcnIyK1euVHXFeTpe4vny27dvj6OjY75l1atXj5o1a+batmbNGmrUqIGuri5mZmb06NGDO3fuvLDOz+rZsyePHj1i7969qm0ZGRls2rSJXr16FXicra0tjRo1ytMCs3btWjw9PfHw8MhzzLhx4zAzM2P58uW5gpynWrVqlaeb3POysrKYMmUKTk5OaGtrY29vz3fffUd6eroqjUKhICAggOTkZNX1zu9eepaamhpjx47lwoULReoelZ6ezoQJE3B2dkZbW5uKFSvy9ddf56pHUe/jiRMn8tVXXwHg4OCgqvPTe+Jp17m1a9dStWpVtLW12bVrF1C0+y8/z9/fz3ZlfP7n+XEpRb3nfv31V5ycnNDV1aV27docPXq00Hq9iLW1Ne7u7kRGRgL/Xd85c+awYMEC1T1x+fJlAK5evUrXrl0xMzNDR0eHmjVrsn379jz5Xrp0iaZNm6Krq0uFChWYOnUqSqUyT7r8xuikpaUxceJEKleujI6ODuXLl6dz585ERERw8+ZNLC0tAZg0aZLqej77vj8fUBfl/oacrn3t27fn33//pXbt2ujo6ODo6MiqVatypcvMzGTSpEm4uLigo6ODubk5H3zwQa7fd0mSXh0Z6EjSe+Kvv/7C0dGR+vXrv5L8u3fvjlKpZObMmdSpU4epU6eyYMECWrRoga2tLT/88APOzs6MHj2aI0eOlFq5CxcupFq1akyePJnp06ejoaHBRx99lKt1avXq1Whra9OwYUNVV5zBgwcXeB6RkZGcOnUq1/Zbt25x4sQJevToodo2bdo0+vbti4uLC/PmzWPUqFHs37+fRo0aFbkLlL29PfXq1WPdunWqbTt37iQhISFXWfnp1asXf/31F0lJSUDOQ9rGjRvzDZCuXbvG1atX8fPzw9DQsEh1y4+/vz/jx4+nevXqzJ8/H19fX2bMmJGrrqtXr6Zhw4Zoa2urrndRumj16tULFxcXJk+enO9YnaeUSiUffvghc+bMUXXB9PPzY/78+XTv3r3Y59S5c2d69uwJwPz581V1fvqQDDld8f73v//RvXt3Fi5cqJoUoyj3X1E0atRIVe7Tn6lTpwJgZWWlSlfUe+73339n8ODBWFtbM2vWLBo0aMCHH35YrCD8eZmZmdy5cwdzc/Nc2wMCAvjpp58YNGgQc+fOxczMjEuXLlG3bl2uXLnCN998w9y5c9HX18fPzy9XIBsdHU2TJk0ICQnhm2++YdSoUaxatYqFCxcWWp/s7Gzat2/PpEmTqFGjBnPnzmXkyJEkJCRw8eJFLC0tWbJkCQCdOnVSXdfOnTsXmGdR7u+nrl+/TteuXWnRogVz587F1NSU/v37c+nSJVWaiRMnMmnSJJo0acKiRYv4/vvvqVSpEmfPni30/CRJKgVCkqR3XkJCggBEx44di5Q+MjJSACIgICDPPkBMmDBB9XrChAkCEIMGDVJty8rKEhUqVBAKhULMnDlTtT0+Pl7o6uqKfv36qbYFBAQIQERGRuYq5+DBgwIQBw8eVG3r16+fsLOzy5UuJSUl1+uMjAzh4eEhmjZtmmu7vr5+rnILKj8hIUFoa2uLL7/8Mle6WbNmCYVCIW7duiWEEOLmzZtCXV1dTJs2LVe60NBQoaGhkWd7QeWeOnVKLFq0SBgaGqrO5aOPPhJNmjQRQghhZ2cn2rVrl+tYQAwdOlTExcUJLS0tsXr1aiGEEDt27BAKhULcvHlT9b7ExsYKIYTYtm2bAMT8+fNfWK8XCQkJEYDw9/fPtX306NECEAcOHFBt69evn9DX1y9Svs+mXblypQDEli1b8pzvU6tXrxZqamri6NGjufJZunSpAERQUJAQonj38ezZs/O9D5+mVVNTE5cuXcqzr6j3n52dXa77L7/7+1mpqamiRo0awsbGRty/f18IUfR7LiMjQ1hZWQkfHx+Rnp6uSvfrr78KQPj6+uZb5vP1bdmypYiNjRWxsbHi/PnzokePHgIQw4cPF0L8d32NjIxETExMruObNWsmPD09RVpammqbUqkU9evXFy4uLqpto0aNEoAIDg5WbYuJiRHGxsZ53g9fX99cdV++fLkAxLx58/LUX6lUCiGEiI2NzfNeP/X0d+Sp4tzfdnZ2AhBHjhzJVe/nPzu8vb3z/P5KkvT6yBYdSXoPJCYmArzUN/mFeXYGMHV1dWrWrIkQggEDBqi2m5iY4Orqyo0bN0qtXF1dXdX/4+PjSUhIoGHDhiX+xtTIyIg2bdqwYcOGXK0Kf/zxB3Xr1qVSpUoAbNmyBaVSSbdu3Xj48KHqx9raGhcXFw4ePFjkMrt160Zqaip///03T5484e+//35ht7WnTE1Nad26tao1KDAwkPr16+c7sUJp3AP//PMPAF988UWu7V9++SVAqYzx6t27d6GtOhs3bsTd3R03N7dc175p06YAxbr2ReXr60uVKlXybC/t+++pzz//nNDQUDZv3oy1tTVQ9Hvu9OnTxMTE8Nlnn+Ua19K/f3+MjY2LXIc9e/ZgaWmJpaUl3t7ebNy4kT59+vDDDz/kStelS5dcrV9xcXEcOHCAbt268eTJE1U9Hz16RKtWrbh27RpRUVFAzj1Vt25dateurTre0tKS3r17F1q/zZs3Y2FhwfDhw/PsK8kYr+Le31WqVKFhw4a56v3855uJiQmXLl3i2rVrxa6PJEkvT47Ck6T3gJGREQBPnjx5ZWU8DQCeMjY2RkdHBwsLizzbHz16VGrl/v3330ydOpWQkJA840RKqnv37vz5558cP36c+vXrExERwZkzZ1iwYIEqzbVr1xBC4OLikm8e+Y2BKYilpSXNmzcnMDCQlJQUsrOz6dq1a5GO7dWrF3369OH27dv8+eefzJo1K990pXEP3Lp1CzU1NZydnXNtt7a2xsTEhFu3bpU476fU1dUZO3Ys/fr1488//6RTp0550ly7do0rV67kerh+VkxMzEvX43kODg75bn8V998vv/xCQEAAv/zyC3Xr1lVtL+o99/R9eD6dpqZmgePP8vO0C6pCoUBPTw93d3dMTEzypHv+2ly/fh0hBOPGjWPcuHH55h0TE4OtrS23bt2iTp06efa7uroWWr+IiAhcXV1LbUKB4t7fz3/mQc6XD/Hx8arXkydPpmPHjlSuXBkPDw9at25Nnz598PLyKpU6S5L0YjLQkaT3gJGRETY2Nly8eLFI6Qt6SMvOzi7wmPxmLitoNrNnv6kvSVlPHT16lA8//JBGjRqxePFiypcvj6amJgEBAS81TXKHDh3Q09Njw4YN1K9fnw0bNqCmpsZHH32kSqNUKlEoFOzcuTPf8zQwMChWmb169WLgwIFER0fTpk2bfB8o8/Phhx+ira1Nv379SE9Pp1u3bvmmc3NzA3KmcH5Zr3pGtN69ezNlyhQmT56Mn59fnv1KpRJPT0/mzZuX7/EVK1YEXu7eet6zLTdPvYr77+TJk4wcORJ/f38GDRqUa19p33OFsbCwoHnz5oWme/7aPJ1IYPTo0bRq1SrfY54PJt4kRb2/i/L51qhRIyIiIti2bRt79uxh2bJlzJ8/n6VLlxa4DpYkSaVHBjqS9J5o3749v/76K8ePH6devXovTGtqagqQZ0B9aXxjX5plbd68GR0dHXbv3p1rvZmAgIA8aYvzcK6vr0/79u3ZuHEj8+bN448//qBhw4bY2Nio0jg5OSGEwMHBgcqVKxc574J06tSJwYMHc+LECf74448iH6erq4ufnx9r1qyhTZs2eVrQnqpcuTKurq5s27aNhQsXluih2M7ODqVSybVr13B3d1dtf/DgAY8fPy61tYietur079+fbdu25dnv5OTE+fPnadas2Qvf1+LcWyUJ3opz/xVFbGwsXbt2xcfHh59//jnP/qLec0/fh2vXrqm680HOZAKRkZF4e3uXqH5F9bTVSFNTs9BAyc7OLt9uXWFhYYWW4+TkRHBwMJmZmQW2oBbnfX1V97eZmRmffPIJn3zyCUlJSTRq1IiJEyfKQEeSXgM5RkeS3hNff/01+vr6+Pv78+DBgzz7IyIiVDMdGRkZYWFhkWd2tMWLF5d6vZycnABylZWdnc2vv/5a6LHq6uooFIpc39DfvHkz34VB9fX1i7UYZPfu3bl37x7Lli3j/PnzeWbz6ty5M+rq6kyaNCnPWBIhRLG75xkYGLBkyRImTpxIhw4dinXs6NGjmTBhQoHdhJ6aNGkSjx49wt/fn6ysrDz79+zZw99//13g8U8X/Xy2Cx+gallp165dser9Ih9//DHOzs5MmjQpz75u3boRFRXFb7/9lmdfamoqycnJQPHuY319fSBvUPQixbn/CpOdnU2PHj3IyMhg8+bN+a4ZU9R7rmbNmlhaWrJ06dJcC8quWLHitSyIamVlRePGjfnll1+4f/9+nv2xsbGq/7dt25YTJ05w8uTJXPvXrl1baDldunTh4cOHLFq0KM++p9dHT08PKNr7+iru7+c/BwwMDHB2ds4zXbUkSa+GbNGRpPeEk5MTgYGBdO/eHXd3d/r27YuHhwcZGRkcO3aMjRs3qtaWgZzJBWbOnIm/vz81a9bkyJEjhIeHl3q9qlatSt26dfn222+Ji4vDzMyM9evX5/sg/rx27doxb948WrduTa9evYiJieHnn3/G2dmZCxcu5Epbo0YN9u3bx7x587CxscHBwSHfsQFPtW3bFkNDQ0aPHo26ujpdunTJtd/JyYmpU6fy7bffcvPmTdW0zZGRkWzdupVBgwYxevToYl2Lfv36FSv9U97e3kX6lr579+6EhoYybdo0zp07R8+ePbGzs+PRo0fs2rWL/fv3v7DLlbe3N/369ePXX3/l8ePH+Pr6cvLkSVauXImfnx9NmjQpUf3zo66uzvfff88nn3ySZ1+fPn3YsGEDn332GQcPHqRBgwZkZ2dz9epVNmzYwO7du1XrHRX1Pq5RowYA33//PT169EBTU5MOHTqoAqD8FOf+K8zSpUs5cOCA6pyeVa5cOVq0aFHke05TU5OpU6cyePBgmjZtqpoyPSAgoFhjdF7Gzz//zAcffICnpycDBw7E0dGRBw8ecPz4ce7evcv58+eBnC9gVq9eTevWrRk5ciT6+vr8+uuv2NnZFXoN+/bty6pVq/jiiy84efIkDRs2JDk5mX379vH555/TsWNHdHV1qVKlCn/88QeVK1fGzMwMDw+PfNeZehX3d5UqVWjcuDE1atTAzMyM06dPs2nTJoYNG1bsvCRJKoEymOlNkqQyFB4eLgYOHCjs7e2FlpaWMDQ0FA0aNBA//fRTrqlgU1JSxIABA4SxsbEwNDQU3bp1EzExMQVOL/10GuOnCppe2NfXV1StWjXXtoiICNG8eXOhra0typUrJ7777juxd+/eIk0v/fvvvwsXFxehra0t3NzcREBAQJ5pY4UQ4urVq6JRo0ZCV1dXAKqpfgua3loIIXr37i0A0bx58wKv5+bNm8UHH3wg9PX1hb6+vnBzcxNDhw4VYWFhBR7zbLmnTp16YboXTS/9IgW9L0IIsX//ftGxY0dhZWUlNDQ0hKWlpejQoYPYtm3bC/MUQojMzEwxadIk4eDgIDQ1NUXFihXFt99+m+veEaLk00s/X5aTk1O+55uRkSF++OEHUbVqVaGtrS1MTU1FjRo1xKRJk0RCQoIqXVHvYyGEmDJlirC1tRVqamq57okXXe+i3n+FTS/99Jj8fp6fDrqo99zixYuFg4OD0NbWFjVr1hRHjhzJM0VzQfK77573dHrp2bNn57s/IiJC9O3bV1hbWwtNTU1ha2sr2rdvLzZt2pQr3YULF4Svr6/Q0dERtra2YsqUKeL3338vdHppIXLe3++//151P1pbW4uuXbuKiIgIVZpjx46JGjVqCC0trVzve37vU1Hv74Kuz/N1nDp1qqhdu7YwMTERurq6ws3NTUybNk1kZGQUdFklSSpFCiFesCqbJEmSJEmSJEnSW0iO0ZEkSZIkSZIk6Z0jAx1JkiRJkiRJkt45MtCRJEmSJEmSJOmdIwMdSZIkSZIkSZLeOTLQkSRJkiRJkiTpnfNWrKOjVCq5d+8ehoaGJVq9WpIkSZIkSZKkd4MQgidPnmBjY4OaWsHtNm9FoHPv3j0qVqxY1tWQJEmSJEmSJOkNcefOHSpUqFDg/rci0DE0NARyTsbIyKiMayNJkiRJkiRJUllJTEykYsWKqhihIG9FoPO0u5qRkZEMdCRJkiRJkiRJKnRIi5yMQJIkSZIkSZKkd44MdCRJkiRJkiRJeufIQEeSJEmSJEmSpHfOWzFGpyiUSiUZGRllXQ1Jkt4QmpqaqKurl3U1JEmSJEkqI+9EoJORkUFkZCRKpbKsqyJJ0hvExMQEa2truf6WJEmSJL2H3vpARwjB/fv3UVdXp2LFii9cNEiSpPeDEIKUlBRiYmIAKF++fBnXSJIkSZKk1+2tD3SysrJISUnBxsYGPT29sq6OJElvCF1dXQBiYmKwsrKS3dgkSZIk6T3z1jd/ZGdnA6ClpVXGNZEk6U3z9MuPzMzMMq6JJEmSJEmv21sf6Dwl++BLkvQ8+bkgSZIkSe+vdybQkSRJkiRJkiRJekoGOpIkSZIkSZIkvXNkoPMesLe3Z8GCBarXCoWCP//886XyLI08pDfX8/eMJEmSJEnS20YGOu+h+/fv06ZNmyKlnThxIj4+Pi+VR2l4Ex+838Q6vW5xcXEMHz4cV1dXdHV1qVSpEiNGjCAhISFXutu3b9OuXTv09PSwsrLiq6++Iisrq4xqLUmSJEnS++Ctn176fZGRkVFqM8tZW1u/EXmUhdK8jmXtTTiXe/fuce/ePebMmUOVKlW4desWn332Gffu3WPTpk1AzsyI7dq1w9rammPHjnH//n369u2LpqYm06dPL9P6S5IkSZL07nrnWnSEEKRkZJXJjxCiyPVs3Lgxw4YNY9iwYRgbG2NhYcG4ceNUedjb2zNlyhT69u2LkZERgwYNAuDff/+lYcOG6OrqUrFiRUaMGEFycrIq35iYGDp06ICuri4ODg6sXbs2T9nPdzu7e/cuPXv2xMzMDH19fWrWrElwcDArVqxg0qRJnD9/HoVCgUKhYMWKFfnmERoaStOmTdHV1cXc3JxBgwaRlJSk2t+/f3/8/PyYM2cO5cuXx9zcnKFDhxZp2t/GjRtz69Yt/ve//6nqAfDo0SN69uyJra0tenp6eHp6sm7dunyv86hRo7CwsKBVq1YAbN++HRcXF3R0dGjSpAkrV65EoVDw+PFj1bEvutYF1akwQUFBNG7cGD09PUxNTWnVqhXx8fGsWrUKc3Nz0tPTc6X38/OjT58+wH+ta8uWLcPBwQEdHR0g/5YlHx8fJk6cCOT8TkycOJFKlSqhra2NjY0NI0aMUKUtyj1TEA8PDzZv3kyHDh1wcnKiadOmTJs2jb/++kvVYrNnzx4uX77MmjVr8PHxoU2bNkyZMoWff/6ZjIyMIpclSZIkSZJUHCVq0fn555+ZPXs20dHReHt789NPP1G7du18065YsYJPPvkk1zZtbW3S0tJKUnShUjOzqTJ+9yvJuzCXJ7dCT6vol3TlypUMGDCAkydPcvr0aQYNGkSlSpUYOHAgAHPmzGH8+PFMmDABgIiICFq3bs3UqVNZvnw5sbGxqmApICAAyAko7t27x8GDB9HU1GTEiBGq1eHzk5SUhK+vL7a2tmzfvh1ra2vOnj2LUqmke/fuXLx4kV27drFv3z4AjI2N8+SRnJxMq1atqFevHqdOnSImJgZ/f3+GDRumCowADh48SPny5Tl48CDXr1+ne/fu+Pj4qM63IFu2bMHb25tBgwblSpuWlkaNGjUYM2YMRkZG7Nixgz59+uDk5JTrfly5ciVDhgwhKCgIgMjISLp27crIkSPx9/fn3LlzjB49OleZhV3rgur0IiEhITRr1oxPP/2UhQsXoqGhwcGDB8nOzuajjz5ixIgRbN++nY8++gjICUB27NjBnj17VHlcv36dzZs3s2XLliIvgLl582bmz5/P+vXrqVq1KtHR0Zw/f161v7j3TGESEhIwMjJCQyPnd+H48eN4enpSrlw5VZpWrVoxZMgQLl26RLVq1UpcliRJkiRJUkGKHej88ccffPHFFyxdupQ6deqwYMECWrVqRVhYGFZWVvkeY2RkRFhYmOq1XNsiR8WKFZk/fz4KhQJXV1dCQ0OZP3++6sG5adOmfPnll6r0/v7+9O7dm1GjRgHg4uLCjz/+iK+vL0uWLOH27dvs3LmTkydPUqtWLQB+//133N3dC6xDYGAgsbGxnDp1CjMzMwCcnZ1V+w0MDNDQ0HhhV7XAwEDS0tJYtWoV+vr6ACxatIgOHTrwww8/qB5wTU1NWbRoEerq6ri5udGuXTv2799faKBgZmaGuro6hoaGuepha2ubK0AZPnw4u3fvZsOGDbkCHRcXF2bNmqV6/c033+Dq6srs2bMBcHV15eLFi0ybNk2VZsaMGS+81gXV6UVmzZpFzZo1Wbx4sWpb1apVVf/v1asXAQEBqkBnzZo1VKpUicaNG6vSZGRksGrVKiwtLYtUJuSMj7G2tqZ58+ZoampSqVIl1fUJDw8v9j3zIg8fPmTKlCmqFkiA6OjoXEEOoHodHR1donIkSZIkSZIKU+xAZ968eQwcOFDVSrN06VJ27NjB8uXL+eabb/I9RqFQFGtMR3p6eq4uPImJiUU+VldTncuTWxU5fWnS1SzaN+xP1a1bN1fQV69ePebOnUt2djYANWvWzJX+/PnzXLhwIVfXIiEESqWSyMhIwsPD0dDQoEaNGqr9bm5umJiYFFiHkJAQqlWrpgpySuLKlSt4e3urghyABg0aoFQqCQsLUz3UVq1aNVcrRPny5QkNDS1xudnZ2UyfPp0NGzYQFRVFRkYG6enp6Onp5Ur37PUACAsLUz3UP/V8i2Rh17okgUBISIgqiMnPwIEDqVWrFlFRUdja2rJixQr69++f6x6xs7MrVpAD8NFHH7FgwQIcHR1p3bo1bdu2pUOHDmhoaHDlypVi3zMFSUxMpF27dlSpUkXVbU6SpPdTZnYmcWlxPE5/TFxaHPFp8cSnxxOfFk9iRiIupi40tG2Itf7bOd5TkqS3Q7ECnYyMDM6cOcO3336r2qampkbz5s05fvx4gcclJSVhZ2eHUqmkevXqTJ8+Pdc32c+bMWMGkyZNKk7VVBQKRbG6j73Jng0cIOc6Dh48ONf4iqcqVapEeHh4scvQ1dUtcf2KS1NTM9drhUKBUqkscX6zZ89m4cKFLFiwAE9PT/T19Rk1alSecR/PX8eiKOxal0Rh17patWp4e3uzatUqWrZsyaVLl9ixY0euNPmdi5qaWp7xYc+OfapYsSJhYWHs27ePvXv38vnnnzN79mwOHz5covPIz5MnT2jdujWGhoZs3bo113ttbW3NyZMnc6V/8OCBap8kSW+v1KxUfjr3E7cTb+cKZpIykwo/GHA1daVRhUY0qtAITwtP1NWK94WhJEnSixQrInj48CHZ2dn5dkO5evVqvse4urqyfPlyvLy8SEhIYM6cOdSvX59Lly5RoUKFfI/59ttv+eKLL1SvExMTqVixYnGq+lYIDg7O9frEiRO4uLgUOPaievXqXL58OVfXsme5ubmRlZXFmTNnVC0WYWFhuQbYP8/Ly4tly5YRFxeXb6uOlpaWqoWpIO7u7qxYsYLk5GTVg3hQUBBqamq4urq+8Niiyq8eQUFBdOzYkY8//hgApVJJeHg4VapUeWFerq6u/PPPP7m2nTp1Ktfrwq51QXV6ES8vL/bv3//CIN7f358FCxYQFRVF8+bNi3TfW1pacv/+fdXrxMREIiMjc6XR1dWlQ4cOdOjQgaFDh+Lm5kZoaGiJ7pnnJSYm0qpVK7S1tdm+fbtqkoSn6tWrx7Rp04iJiVF1b927dy9GRkaFvleSJL3Z1lxew+rLq/Pdp65Qx1jbGDMdM0x1TDHVNsVUxxRdDV3OxpwlNDaUsPgwwuLD+C30N0y1TWlg24BGFRpR36Y+xtp5x4RKkiQVxytv+qhXrx716tVTva5fvz7u7u788ssvTJkyJd9jtLW10dbWftVVK3O3b9/miy++YPDgwZw9e5affvqJuXPnFph+zJgx1K1bl2HDhuHv74++vj6XL19m7969LFq0CFdXV1q3bs3gwYNZsmQJGhoajBo16oUtCT179mT69On4+fkxY8YMypcvz7lz57CxsaFevXrY29sTGRlJSEgIFSpUwNDQMM9707t3byZMmEC/fv2YOHEisbGxDB8+nD59+uQJikvK3t6eI0eO0KNHD7S1tbGwsMDFxYVNmzZx7NgxTE1NmTdvHg8ePCj04Xnw4MHMmzePMWPGMGDAAEJCQnLNJgeFX+uC6vQi3377LZ6ennz++ed89tlnaGlpcfDgQT766CPVsb169WL06NH89ttvrFq1qkjXpmnTpqxYsYIOHTpgYmLC+PHjcwXLK1asIDs7mzp16qCnp8eaNWvQ1dXFzs4Oc3PzYt8zz0pMTKRly5akpKSwZs0aEhMTVV1NLS0tUVdXp2XLllSpUoU+ffowa9YsoqOjGTt2LEOHDn0vfs8l6V2VkpnCqss5n1P9q/bHx8oHMx0zTLRNMNMxw1DLEDVFwZO7xqXFERQVxOG7hzkWdYz49Hj+vvE3f9/4G3WFOj5WPjmtPbaNcDYt+EsnSZKkghRremkLCwvU1dVV3U6eevDgQZG7oGhqalKtWjWuX79enKLfSX379iU1NZXatWszdOhQRo4cmWsQ9/O8vLw4fPgw4eHhNGzYkGrVqjF+/HhsbGxUaQICArCxscHX15fOnTszaNCgAieJgJxWiT179mBlZUXbtm3x9PRk5syZqgflLl260Lp1a5o0aYKlpWWe6ZsB9PT02L17N3FxcdSqVYuuXbvSrFkzVUBQGiZPnszNmzdxcnJSjVEZO3Ys1atXp1WrVjRu3Bhra2v8/PwKzcvBwYFNmzaxZcsWvLy8WLJkCd9//z2A6sG7KNc6vzq9SOXKldmzZw/nz5+ndu3a1KtXj23btqlmJ4OcWe26dOmCgYFBkc4FcgIoX19f2rdvT7t27fDz88PJyUm138TEhN9++40GDRrg5eXFvn37+OuvvzA3NweKf8886+zZswQHBxMaGoqzszPly5dX/dy5cwcAdXV1/v77b9TV1alXrx4ff/wxffv2ZfLkyUUqQ5KkN9PG8I08Tn9MJcNKjKw+kmaVmlHNqhoOxg4Yaxu/MMgBMNMxo4NTB+b4zuFwj8Msb7WcT6p+gqOxI9kimzMPzjD/zHw6be/E14e/JjUr9TWdmSRJ7wqFKM7iL0CdOnWoXbs2P/30E5DTXahSpUoMGzaswMkInpWdnU3VqlVp27Yt8+bNK1KZiYmJGBsbq6atfVZaWhqRkZG51hV5GzRu3BgfH588659IZWPatGksXbpU9XBelpo1a0bVqlX58ccfy7oqb7239fNBkt50aVlptNnShoepD5lcfzKdXDqVav53n9zlyN0jHIk6QvC9YLJEFlXMq/Bjkx8pp186PQUkSXp7vSg2eFaxFwz94osv+O2331i5ciVXrlxhyJAhJCcnq2Zh69u3b67JCiZPnsyePXu4ceMGZ8+e5eOPP+bWrVv4+/uX4LQkqXQsXryYU6dOcePGDVavXs3s2bPp169fmdYpPj6erVu3cujQIYYOHVqmdZEkSXqRrde38jD1IeX1y9PesX2p51/BsAK93HuxtPlSfm35KybaJlx+dJmeO3oSGlvy2TolSXq/FDvQ6d69u2ohSx8fH0JCQti1a5dqLMbt27dzDYyOj49n4MCBuLu707ZtWxITEzl27JgchCypHD16FAMDgwJ/XoVr167RsWNHqlSpwpQpU/jyyy9fekrkNm3aFHgO06dPL/T4atWq0b9/f3744YdSm8ThZa1du7bAc3rRzImSJL27MrMzWX5xOQADPAagqa5ZyBEvp5Z1Lda1W4eziTOxqbH039WfHTd2FH6gJEnvvWJ3XSsL72LXNek/qampREVFFbj/RTOfvUmioqJITc2/D7mZmdlLrVVUVp48eZJnTN5Tmpqa2NnZveYaFY/8fJCk0rc5fDMTj0/EUteSnV12oq3+eiYVSc5M5psj33Do7iEABnoOZFi1YYWOBZIk6d1T1K5r78aCM9JbTVdX960JZl7E1ta2rKtQ6gwNDTE0NCzrakiS9IbIUmaxLHQZkDPT2usKcgD0NfVZ0GQBP537id8v/s5vob9x/fF1ZjaciZ6mXuEZSJL03pFfg0iSJEmSVCQ7I3dyN+kuZjpmdK3c9bWXr66mzqgao5j+wXQ01TQ5eOcgfXb2ISqp4F4BkiS9v2SgI0mSJElSobKV2fwW+hsAfar0KdNWlA5OHQhoHYC5jjnh8eH0/LsnZx+cLbP6SJL0ZpKBjiRJkiRJhdp3ex+RCZEYaRnRw7VHWVcHb0tv1rdfj7uZO/Hp8QzYM4Ct17aWdbUkSXqDyEBHkiRJkqQXEkLw64VfAfjY/WMMtF7NjJjFZa1vzYrWK2hh14IsZRbjj41n1qlZPMl4UtZVkyTpDSAnI5AkSZIk6YUO3TlEeHw4+pr69HLvVdbVyUVPU485vnP45fwvLD6/mNWXV7P68mqs9KxwNnFW/TiZOOFk4oS+pn5ZV1mSpNdEBjrvAXt7e0aNGsWoUaMAUCgUbN26FT8/vxLnWRp5SLndvHkTBwcHzp07h4+PT1lXR5IkCcjdmtPDtQfG2sZlXKO81BRqDPEZgpOJE/POzCMqKYqYlBhiUmI4du9YrrQ2+jY4mTipgh9nU2ecjJ3Q0SidKejj0uI4FX2Kk/dPcjL6JA9SHjCv8Tw+sP2gVPKXJKnoZKDzHrp//z6mpqZFSjtx4kT+/PNPQkJCSpxHaXg+WHsTlHadKlasyP3797GwsChS+v79+/P48WP+/PPPUim/IJmZmYwdO5Z//vmHGzduYGxsTPPmzZk5cyY2NjaqdHFxcQwfPpy//voLNTU1unTpwsKFC1/Zoq+SJL0ex+8d5+Kji+io69CnSp+yrs4LtbRvSUv7liRmJHLj8Q2uP75OxOMIrj2+RsTjCB6mPuRe8j3uJd/jaNRR1XFqCjXsjexxNXWlslll3MzccDV1xULXAoVC8cIyE9ITOB19mpPROYHN9cfX86T57uh3bP5wM5Z6lqV+zpIkFUwGOm+JjIwMtLS0SiUva2vrNyKPslCa17G0qaurl8l1LeyapKSkcPbsWcaNG4e3tzfx8fGMHDmSDz/8kNOnT6vS9e7dm/v377N3714yMzP55JNPGDRoEIGBga/jNCRJegWEEPxy4RcAPnL9CHNd8zKuUdEYaRnhY+WDj5VPru0J6Qlcf3yd6/HXc4KghAiuxV/jcfpjbiTc4EbCDXbe3KlKb6ZjRmXTyriauuJq5kpl08qU0yvH+djznIw+yanoU1yNu4og99rrlU0rU9u6NjWta7IkZAlh8WF89+93/NLiF7nAqSS9TuItkJCQIACRkJCQZ19qaqq4fPmySE1NzdmgVAqRnlQ2P0plkc/J19dXDB06VAwdOlQYGRkJc3NzMXbsWKH8/zzs7OzE5MmTRZ8+fYShoaHo16+fEEKIo0ePig8++EDo6OiIChUqiOHDh4ukpCRVvg8ePBDt27cXOjo6wt7eXqxZs0bY2dmJ+fPnq9IAYuvWrarXd+7cET169BCmpqZCT09P1KhRQ5w4cUIEBAQIINdPQEBAvnlcuHBBNGnSROjo6AgzMzMxcOBA8eTJE9X+fv36iY4dO4rZs2cLa2trYWZmJj7//HORkZFRpGv1fD2EEOLhw4eiR48ewsbGRujq6goPDw8RGBiY73UeOXKkMDc3F40bNxZCCLFt2zbh7OwstLW1RePGjcWKFSsEIOLj41XHvuhaF1SngiQkJAgdHR3xzz//5Nq+ZcsWYWBgIJKTk0VkZKQAxLlz51T7L168KNq1aycMDQ2FgYGB+OCDD8T169fFhAkT8pR/8ODBYr0XU6dOFeXLlxf29vaFvgfPO3nypADErVu3hBBCXL58WQDi1KlTqjQ7d+4UCoVCREVFFTv/0pLn80GSpGI5ef+k8FjhIaqtqiYeJD8o6+q8EkqlUjxIfiCO3Dkifrvwm/jq0Feiw9YOwmull/BY4VGknw+3fiimHJ8idkfuFo9SH+XKPyI+QtRcXVN4rPAQy0OXl9FZStK75UWxwbPevRadzBSYblN4ulfhu3ugVfRBjitXrmTAgAGcPHmS06dPM2jQICpVqsTAgQMBmDNnDuPHj2fChAkARERE0Lp1a6ZOncry5cuJjY1l2LBhDBs2jICAACCnO9O9e/c4ePAgmpqajBgxgpiYmALrkJSUhK+vL7a2tmzfvh1ra2vOnj2LUqmke/fuXLx4kV27drFv3z4AjI3z9s1OTk6mVatW1KtXj1OnThETE4O/vz/Dhg1jxYoVqnQHDx6kfPnyHDx4kOvXr9O9e3d8fHxU51uQLVu24O3tzaBBg3KlTUtLo0aNGowZMwYjIyN27NhBnz59cHJyonbt2rmu85AhQwgKCgIgMjKSrl27MnLkSPz9/Tl37hyjR4/OVWZh17qgOhXEyMiI9u3bExgYSJs2bVTb165di5+fH3p6edejiIqKolGjRjRu3JgDBw5gZGREUFAQWVlZjB49mitXrpCYmKh6783MzIr8Xuzfvx8jIyP27t1baN3zk5CQgEKhwMTEBIDjx49jYmJCzZo1VWmaN2+OmpoawcHBdOrUqUTlSJJUtp6Ozens0hkrPasyrs2roVAosNKzwkrPioYVGqq2p2alEvE4grC4MMLiwwiLCyM8PpykzCQqGVaidvna1LauTS3rWljoFtzl2NHEkTG1xzDp+CR+PPsjta1rU9Wi6us4NUl6oZiUGCx0Ld7pVsZ3L9B5i1SsWJH58+ejUChwdXUlNDSU+fPnqx6cmzZtypdffqlK7+/vT+/evVVjQlxcXPjxxx/x9fVlyZIl3L59m507d3Ly5Elq1aoFwO+//467u3uBdQgMDCQ2NpZTp05hZmYGgLOzs2q/gYEBGhoaL+xSFRgYSFpaGqtWrUJfPyfQW7RoER06dOCHH36gXLlyAJiamrJo0SLU1dVxc3OjXbt27N+/v9BAwczMDHV1dQwNDXPVw9bWNleAMnz4cHbv3s2GDRtyBTouLi7MmjVL9fqbb77B1dWV2bNnA+Dq6srFixeZNm2aKs2MGTNeeK0LqtOL9O7dmz59+pCSkoKenh6JiYns2LGDrVvzX/fh559/xtjYmPXr16OpqQlA5cqVVft1dXVJT0/PVf7KlSuL9F7o6+uzbNmyEnXjS0tLY8yYMfTs2RMjIyMAoqOjsbLK/RCkoaGBmZkZ0dHRxS5DkqSydz72PCfun0BDocGnHp8WmC7xYSo6Bppo6bxbjxS6Grp4WHjgYeGh2iaEIDkzudjTa3dx6cKxe8fYe2svXx/5mg0dNsjZ36QytTl8MxOPT8TL0otpDaZhb2xf1lV6Jd6tTyUATb2clpWyKrsY6tatm2uQY7169Zg7dy7Z2dkAub4dBzh//jwXLlxg7dq1qm1CCJRKJZGRkYSHh6OhoUGNGjVU+93c3FTfuucnJCSEatWqqYKckrhy5Qre3t6qB2uABg0aoFQqCQsLUz1cV61aFXV1dVWa8uXLExoaWuJys7OzmT59Ohs2bCAqKoqMjAzS09PztI48ez0AwsLCVIHgU88GRlD4tX5R8FiQtm3boqmpyfbt2+nRowebN2/GyMiI5s2b55s+JCSEhg0bqoKcoijqe+Hp6VmiICczM5Nu3bohhGDJkiXFPl6SpLfH09acDk4dsDHIv6fE7UuP+HvReTS01HGra41H4wqYlX93H+AVCkWJ1hBSKBRMqDeB0Ieh3H5ym+nB05n2wbTCD5SkVyBTmcnSC0sBuBB7gY/++ohRNUbR063nO9e68+4FOgpFsbqPvcmefViFnG5mgwcPZsSIEXnSVqpUifDw8GKXoaurW+L6FdfzD+wKhQKlUlni/GbPns3ChQtZsGABnp6e6OvrM2rUKDIyMnKle/46FkVh17oktLS06Nq1K4GBgfTo0YPAwEC6d++Ohkb+v4av8r0pyTV5GuTcunVL1ZXuKWtr6zxdJLOysoiLi3trJ66QpPfZlUdXOHL3CGoKNfw9/fNNI5SC439GIARkpmcTejiK0MNRVHAzxbNxBey9LFBTe/GMZe8TY21jZjacyae7P2V7xHYa2DSgrWPbsq6W9B7afXM30cnRmOmY4WLqQvD9YGaenMnB2weZ3GBygV9svI3erbDtLRMcHJzr9YkTJ3BxccnV6vGs6tWrc/nyZZydnfP8aGlp4ebmRlZWFmfOnFEdExYWxuPHjwusg5eXFyEhIcTFxeW7X0tLS9XCVBB3d3fOnz9PcnKyaltQUBBqamq4urq+8Niiyq8eQUFBdOzYkY8//hhvb28cHR2LFOy5urrmmi0M4NSpU7leF3atC6pTYXr37s2uXbu4dOkSBw4coHfv3gWm9fLy4ujRo2RmZua7P7/yX9V78TTIuXbtGvv27cPcPPfMS/Xq1ePx48e57r0DBw6gVCqpU6dOicuVJKls/Bb6GwBtHNpQySj/L3duhMTy8E4SmjrqtPnMEwdvCxQKuHs1np1LQ1kz9jhnd98iLSn/z7D3UY1yNRjkNQiAKSemcPfJ3TKukfS+EUKw8tJKAHq79+bXFr/yXZ3v0FHXITg6mM7bO7P12laEEIXk9HaQgU4Zun37Nl988QVhYWGsW7eOn376iZEjRxaYfsyYMRw7doxhw4YREhLCtWvX2LZtG8OGDQNyHuBbt27N4MGDCQ4O5syZM/j7+7+wZaBnz55YW1vj5+dHUFAQN27cYPPmzRw/fhzIWSsmMjKSkJAQHj58SHp6ep48evfujY6ODv369ePixYscPHiQ4cOH06dPH1VXqZdlb2/PkSNHiIqK4uHDh0DOuJm9e/dy7Ngxrly5wuDBg3nw4EGheQ0ePJirV68yZswYwsPD2bBhg2qg/tOuhIVd64LqVJhGjRphbW1N7969cXBweGEQMGzYMBITE+nRowenT5/m2rVrrF69mrCwMFX5Fy5cICwsjIcPH5KZmflK3ovMzEy6du3K6dOnWbt2LdnZ2URHRxMdHa1qPXN3d6d169YMHDiQkydPEhQUxLBhw+jRo0eutXYkSXrzXY+/zt5bOROVDPTMfwylUikI/isSAO9mFXH0saTtEC8+nlKPai0roa2vwZO4NI5vjWDFt0EcWH2F2DtPXts5vMkGew2mmlU1kjKT+OboN2Qps8q6StJ75MT9E1yNu4quhi7dKndDTaFGT7eebPpwE96W3iRnJjP+2HhGHBjBw9SiPdu8yWSgU4b69u1LamoqtWvXZujQoYwcOZJBgwYVmN7Ly4vDhw8THh5Ow4YNqVatGuPHj8/1IBkQEICNjQ2+vr507tyZQYMG5Rkk/iwtLS327NmDlZUVbdu2xdPTk5kzZ6palbp06ULr1q1p0qQJlpaWrFu3Lk8eenp67N69m7i4OGrVqkXXrl1p1qwZixYteomrk9vkyZO5efMmTk5OWFrmLLg2duxYqlevTqtWrWjcuLEqYCuMg4MDmzZtYsuWLXh5ebFkyRK+//57ALS1tYGiXev86lQYhUJBz549OX/+/AtbcwDMzc05cOCAama8GjVq8Ntvv6m6AA4cOBBXV1dq1qyJpaUlQUFBr+S9iIqKYvv27dy9excfHx/Kly+v+jl27L8Vx9euXYubmxvNmjWjbdu2fPDBB/z6668lLleSpLLxtDWnhV0LnEyc8k1z7dQD4u8no62ngU+ziqrtRha61O/sTP8ZDWjSxw2LigZkZyq5EnSfDdNOsWXOGa6dfkB2dsm7Lb/tNNQ0mNlwJoaahpyPPc+S83K8o/T6PG3N6eTcCRMdE9V2OyM7VrZeyajqo9BU0+TQ3UP4bfNj181dZVTT0qEQb0HbVGJiIsbGxiQkJOQaFwA5M0BFRkbi4OCAjo5OGdWw+Bo3boyPjw8LFiwo66pIwLRp01i6dCl37twp66pIpeht/XyQpLKgFEo2hW9iWvA0lELJhvYbcDfPO/FKdraSdRODSYhNpa6fIzVa2xeYpxCC6IgELhy6y42zsSiVOY8cRhY61Ghtj2tda9Q13s/vXHfd3MVXh79CgYLfW/1OLetahR8kSS8hLC6Mrn91RU2hxo5OO6hgWCHfdOHx4Xz/7/dcjbsKQBv7Nnxf93uMtfMuMVJWXhQbPOv9/HSR3nuLFy/m1KlT3Lhxg9WrVzN79mz69etX1tWSJEkqEzcTbvLp7k+ZcmIKSqGknWO7fIMcgLAT0STEpqJrqIln4/wflJ5SKBSUdzahlb8HfafXp2Y7e3QNNUl8mMbBNVdZO/4EF49EkZ35/rXwtLZvTSfnTggE3x79loT0hLKukvSOW3V5FZDTWltQkANQ2bQygW0DGew1GHWFOjtv7sRvmx9H7h55XVUtNTLQkcrc0aNHMTAwKPDnVbh27RodO3akSpUqTJkyhS+//JKJEye+VJ5t2rQp8BymT59eOhV/Bcri+kuS9GbIVGayLHQZXbZ34cyDM+hq6PJN7W+Y1iD/qY+zM5Wc2pEzNqd6K7tirZ2jb6JNnQ6O9JlWnwZdndEz0uJJXBqHA8NYM/44oYfuvncBzze1v8HeyJ4HKQ+YcGzCOzMAXHrzRCdH88+NfwDoX7V/oek11TUZVm0Yq9usxsHYgYepDxm6fyg/nPzhFde0dMmua1KZS01NJSoqqsD9zy5g+iaLiooiNTU1331mZmYvtVbRq/SuXP/8yM8HSSrYpUeXmBA0gbD4nAlOGtg0YFy9cdga2BZ4TOihuxxZH46esRZ9ptRDQyv/WUKLIisjm0v/3uPc7lskJ+RMbKJvok31VnZU+aA8Gpolz/ttcvnRZXr/05ssZRbj6o6jm2u3sq6S9A6ad3oeAZcCqFmuJgGtA4p1bFpWGj+d+4nVl1czod4EulTu8opqWXRF7bomAx1Jkt5Z8vNBkvJKzUplScgSVl5eiVIoMdY2ZkytMbR3bJ9rEevnZWVks3rccVISMmjUo3Kh3daKKiszmytB9zmz6xbJj3Nm9tQz1qJ6SzuqNrR5qWDqbbHy0krmnJ6DjroO69uvL3ASCEkqiaSMJFpsakFSZhKLmi7Ct6JvifK5GncVV1PXF35OvC5FDXTevQVDJUmSJEnK18n7J5l4fCJ3nuRMvNLGvg1jao/BXNe8kCPh4pEoUhIyMDTToUqD0ps2XkNTHc/GFajSwIYrx+5xZtctkuLT+XfjNc7svkX1lpXw8LV9p1t4+lTpw7F7xzh27xhfHPqC5nbNVd3YBAIhRP7/IjDTMaNflX5oqmsWUor0vtp8bTNJmUk4GjvSsELDEufjZuZWirV6PWSgI0mSJEnvuMSMROadnsfma5sBsNKzYnzd8UX+ZjcjLYszu24BULOdPeqapT/EV11TDQ/fCrg3sOHq8fuc2XmLJ3FpBG26zo2QWDoM90FT+90MdtQUakz7YBpdtnfhRsINfr1QvKn5H6U+YkztMa+odtLbLFOZyerLqwHoV7Ufaoqc3930lExCD0ehb6yFRQVDzMrrv5Lf67ImAx1JkiRJKiVCCCITItHX1KecfuksmPyyDt4+yJQTU4hNjQWgu2t3RlUfhYFW0ScbuXDwLmlJmRhb6uJW1/pVVRUAdQ01qja0xa1eecJORBO0+Tr3ryew4+fztBvmjeY72pXNQteCxc0Wsz1iO0qhRE2hhkKhQEFON6Gn/1egyHlYVUBKZgp/hP3BmitrqFu+bom7JEnvrl2Ru3iQ8gBzHXPaO7ZXbT/yRzjhwf8tsq6mpsC0vB4WFQyxqGiARQUDLCoYomPwdrcUykBHkiRJkl7Cw9SHnLh/guP3jnP83nFiU2Mx1DLk705/Y6ZTtpOQBN8PZsTBEQDYG9kzsf5EapSrUaw80lMyCdl7G4Ba7R1QU3893/qqa6hR5QMbzGz1+WthCFHhj9nx8wXaDfV644Od5IR07l6NJyosnidxaTTo6oJFhcIDy6oWValqUbVYZWmqabLmyhrGBo1lU4dNb0yALZU9IYRqgdDe7r3RUtcCIOZWoirIsXEx4dG9JNKTs3gUlcyjqGTCgv/Lw8BUG4uKhv8f+BhgZW+EodnbM+ZVBjqSJEmSVAxpWWmcjTmrCmyezlr2rCcZT1hzeQ0jqo8ogxrmSM9OZ8qJKQC0c2zHpPqT0FbXLnY+IfvvkJ6ShWl5fVxqvf6HaGsHYzqM8GH7whCiwuLZueQCbT/3eqPG7KQlZ3Iv/DF3w+K5ezWO+OiUXPu3LzyH3/+qY2ajX+pl/6/G/zjz4AxX4q7wzdFvWNZyGepqb861kcrO8fs5n0+6Grqq2fyEEBzbch2AyrXL0eLTqgghSIpP5+HdJB7eeaL6N/FhGknx6STFp3PzwkMAqjS0oUnvt2esjgx03gP29vaMGjWKUaNGATnN31u3bsXPz6/EeZZGHtK74/l7TJLeJUqh5Fr8NY7dO8bxe8c5G3OW9Oz0XGnczdypZ1OPejb1iEuNY8zRMay/up5PPT4tVhex0rQ8dDm3Em9hqWvJ93W+L1GQk5qUwfn9ORMX1OnggJpa2cy2ZO1oTPvh3vz103nuXIln59JQ2nzmWWbBTmZ6NvevPw1s4om98wSencNWAZYVDbF1NSUqLJ7Y20/YtuAcnb6sjkk5vVKti5a6FrN9Z9Ptr26cfnCaX0N/ZYj3kFItQ3o7PW3N6ezSGWNtYwBuXXxEVNhj1DXUqNPREch5pjM008HQTAcHLwvV8empWTy6m8TDu094eCeJh3eTKGdX8Axnb6J3b9SRVKj79+/Tpk2bIqWdOHEiPj4+L5VHabC3t2fBggWvrbyiKG6dVqxYgYmJySurz6vy22+/0bBhQ0xNTTE1NaV58+acPHmy1PKPi4tj+PDhuLq6oqurS6VKlRgxYgQJCblXCb99+zbt2rVDT08PKysrvvrqK7KyskqtHpKUn0xlJoP2DKLrX12Zd2Yex+8fJz07HSs9K/yc/fih4Q8c6naIDR028L8a/6Nu+bq0dmiNo7EjTzKf8EfYH2VS71uJt/gt9DcAvq79NYZahiXK59ye22SmZWNR0QBHH8vSrGKx2Tib0GGYFxpaaty+FMeuXy++1gVGszKzuRx0jy1zzrDsiyP89dN5zu25TeztnCDH1FoPD19bWg/2YMDshnT7rhYNujjz4QgfzG31SUnMYNuCcyQ+zH+9tZdhZ2TH2LpjAVh6filnHpwp9TKkt0tYXBjH7h1DTaFGnyp9AFBmKzm2JQIAr6YVMDLXfWEe2roa2LiY4NWkIk37utPtu1pU+aD0Zlx8HWSLzlsiIyMDLS2tUsnL2vrlB5KWRh5loTSv47suOzsbhULBoUOH6NmzJ/Xr10dHR4cffviBli1bcunSJWxtC15YsKju3bvHvXv3mDNnDlWqVOHWrVt89tln3Lt3j02bNqnq0q5dO6ytrTl27Bj379+nb9++aGpqMn369JeugyQVJPBKIMHRwWipaVHXpi71ytejvk19HIwdClxLQk2hxgDPAXz/7/esuryK3u690dF4fX3ahRBMOTGFTGUmDWwa0MquVYnySU5IJ/TgXQDqdHBEUUatOc+ycTGl/VBv/l50nluhj9j120VaD/JAXePVfW+blpTJxSN3uXAoitTEDNV2A1NtKriZUsHNDNvKphiY5t9ipmOgyYcjq/HnvLPER6fw5/yclp3SHufQwakDJ+6fYHvEdsYcGcOmDpsw0TEp1TKkt8fT1pyWdi1ViwBfOXaf+PvJ6OhrUqO1XVlW7/URb4GEhAQBiISEhDz7UlNTxeXLl0VqaqoQQgilUimSM5LL5EepVBb5nHx9fcXQoUPF0KFDhZGRkTA3Nxdjx45V5WFnZycmT54s+vTpIwwNDUW/fv2EEEIcPXpUfPDBB0JHR0dUqFBBDB8+XCQlJanyffDggWjfvr3Q0dER9vb2Ys2aNcLOzk7Mnz9flQYQW7duVb2+c+eO6NGjhzA1NRV6enqiRo0a4sSJEyIgIECQ0xiv+gkICMg3jwsXLogmTZoIHR0dYWZmJgYOHCiePHmi2t+vXz/RsWNHMXv2bGFtbS3MzMzE559/LjIyMop0rZ6vhxBCPHz4UPTo0UPY2NgIXV1d4eHhIQIDA/O9ziNHjhTm5uaicePGQgghtm3bJpydnYW2trZo3LixWLFihQBEfHy86tgXXeuC6lSQgwcP5kk/YcIEIYQQaWlp4ssvvxQ2NjZCT09P1K5dWxw8eFB1bFHOMzExUfTq1Uvo6ekJa2trMW/ePOHr6ytGjhypSlNYOQEBAcLY2Fhs27ZNuLu7C3V1dREZGZnnXLKysoShoaFYuXKlatvT+7VHjx5CT09P2NjYiEWLFr3wmrzIhg0bhJaWlsjMzBRCCPHPP/8INTU1ER0drUqzZMkSYWRkJNLT0wvM5/nPB0kqjvtJ90XtNbWFxwoPsTl8c7GOzcjOEC03thQeKzxE4JXAwg8oRX9H/C08VniIGqtriNsJt0ucz5H1YWLR4P1i48xTxfr79jrcvvJILBl2UCwavF/sWHxeZGVll3oZj2OSxeHAq2Lp/5ezaPB+seKbf8WZXTdF/IPi/c0XQoik+DSxeuwxsWjwfrF67DGRFJ9WKvVUZv9Xj+SMZNFuSzvhscJDDNs/7I1736TX437SfeGz0kd4rPAQF2MvCiGESE/NFL9/dVQsGrxfhOwr+efCm+JFscGz3rkWndSsVOoE1imTsoN7BaOnWfS+tytXrmTAgAGcPHmS06dPM2jQICpVqsTAgQMBmDNnDuPHj2fChAkARERE0Lp1a6ZOncry5cuJjY1l2LBhDBs2jICAAAD69+/PvXv3OHjwIJqamowYMYKYmJgC65CUlISvry+2trZs374da2trzp49i1KppHv37ly8eJFdu3axb98+AIyNjfPkkZycTKtWrahXrx6nTp0iJiYGf39/hg0bxooVK1TpDh48SPny5Tl48CDXr1+ne/fu+Pj4qM63IFu2bMHb25tBgwblSpuWlkaNGjUYM2YMRkZG7Nixgz59+uDk5ETt2rVzXechQ4YQFBQEQGRkJF27dmXkyJH4+/tz7tw5Ro8enavMwq51QXUqSP369VmwYAHjx48nLCxn4LKBQU6//WHDhnH58mXWr1+PjY0NW7dupXXr1oSGhuLi4lKk8/ziiy8ICgpi+/btlCtXjvHjx3P27Nlc3Q4LKwcgJSWFH374gWXLlmFubo6VlVWec0lJSSEzMxMzs9yzSc2ePZvvvvuOSZMmsXv3bkaOHEnlypVp0aJFodfneU9XOtbQyPmIOn78OJ6enpQr999A6FatWjFkyBAuXbpEtWrVil2GJBVm1qlZpGSl4G3pjZ+zX7GO1VTT5BOPT5gWPI2AiwF0rdwVTbVXP01rQnoCs07NAmCQ1yAqGlUsUT5P4tK4eDQKgDofOr4RK6E/q6KbGW2HePLP4lAizz9k77JLtPCvinopzAh3PyKBkH23uRESqxp3Y1HRAJ/mlXCuaVXiMvRNtOn4v2psnXuWhNhUti04h98X1dEzKlkvg6jweI5tiSAhJoUPR/pgZWeEnqYesxvNpvc/vTl05xDrrq6jl3uvEuUvvb3WXllLlsiilnUt1Sx+IXtvk5qYgZGlLh6+L98b423xzgU6b5OKFSsyf/58FAoFrq6uhIaGMn/+fNWDc9OmTfnyyy9V6f39/endu7dqwLeLiws//vgjvr6+LFmyhNu3b7Nz505OnjxJrVq1APj9999xd3cvsA6BgYHExsZy6tQp1YOrs7Ozar+BgQEaGhov7KoWGBhIWloaq1atQl8/Z0aZRYsW0aFDB3744QfVw6mpqSmLFi1CXV0dNzc32rVrx/79+wsNFMzMzFBXV8fQ0DBXPWxtbXMFKMOHD2f37t1s2LAhV6Dj4uLCrFmzVK+/+eYbXF1dmT17NgCurq5cvHiRadOmqdLMmDHjhde6oDoVREtLC2NjYxQKRa70t2/fJiAggNu3b2Njk9PvdfTo0ezatYuAgACmT59e6Hk+efKElStXEhgYSLNmzQAICAhQ5VfUcgAyMzNZvHgx3t7eBZ7LmDFjsLGxoXnz5rm2N2jQgG+++QaAypUrExQUxPz584sd6Dx8+JApU6YwaNAg1bbo6OhcQQ6geh0dHV2s/CWpKP6N+pe9t/airlBnXN1xqkX2isPP2Y+l55dyP/k+OyN38qHTh6+gprn9ePZH4tLicDR25JOqn5Q4nzM7b6LMEti4mFDBzbQUa1h6KlUxp81nnvyz9AIR52JRLL9Mi0+rlGj6a6VSEHk+lpC9t4m+kajabudhjk/ziti6mpZKsGdopoPf/wc78dEpqtnYirNWSdz9ZI5vjVDNggWwf+UVun1bC3VNNdzN3fmy5pfMPDmTOafnUL1c9bdyRXupZJ5kPGFj+EYA+lftD+R0Qz33/1PE1/NzeqVdPd8071ygo6uhS3Cv4MITvqKyi6Nu3bq5Pjjr1avH3Llzyc7OBqBmzZq50p8/f54LFy6wdu1a1TYhBEqlksjISMLDw9HQ0KBGjf/WSHBzc3vhAPiQkBCqVauW59v54rhy5Qre3t6qIAdyHnqVSiVhYWGqB9KqVauirv7fDDnly5cnNDS0xOVmZ2czffp0NmzYQFRUFBkZGaSnp6Onl7tV7dnrARAWFqYKBJ96NjCCwq/1i4LH4ggNDSU7O5vKlSvn2p6eno65uTlQ+HneuHGDzMzMXOdgbGyMq6trscqBnIDMy8urwPrOnDmT9evXc+jQIXR0cvcvr1evXp7XxZ1AIjExkXbt2lGlShUmTpxYrGMlqbSkZaUxPTgn+O/l3gtXM9dCjsifjoYOfar0YcHZBSwLXUZ7x/YlCpiK6nzsedUDzti6Y9FUL1kLUkJsKleC7gNvZmvOs+w8zGkzyJOdv4Ry/UwMCjUFzT+pUujscFmZ2aQkZJCSmEHMrUTOH7hLYmzOJAFqGgpc61jj06zSK5kO2shCl46jcoKdR1HJbP8xhI6jfNDWe/H7lZyQzqm/I7kcdB+hFCjUFFT5wIYb52KIu5fMyR2R1PNzAqCXWy9O3DvBobuH+OrwV/zR/o9i9TiR3l6bwzeTnJmMk7ETH9h+AMDJvyLJylBi7WiEU/WynVTkdXvnAh2FQvHO/DI/GzhATjezwYMHM2JE3nUZKlWqRHh4eLHL0NUtXnD2MjQ1c3+IKxQKlMqSz5gze/ZsFi5cyIIFC/D09ERfX59Ro0aRkZGRK93z17EoCrvWpSUpKQl1dXXOnDmTKwiE/7q2FfU8X7YcyLkfCnqomTNnDjNnzmTfvn0vDIZK6smTJ7Ru3RpDQ0O2bt2a636xtrbOM9PbgwcPVPskqTQtv7icO0/uYKVrxVCfoS+VV3fX7vwe+juRCZEcuH2A5nbNCz+oBLKUWUw+PhmBoKNTR2pZ1yr8oAKc3hGJUimoWMUMGxeT0qvkK2LvZUHrQR7s+uUi1049QKEG7vXKk5KYQXJCBikJ6Tn/Jub8PyUxg/SUvDM2autr4OlbAQ9fW/SNiz8Vd3GYlNOj46hq/Dn/LLG3n/DXT+f5cKQPWjp5H8sy07MJ2Xebs3tuk5We80Wog7cF9To5YWqtTyV3M3b+Esq53bdw9LGknL0RCoWCKQ2m0OWvLtxMvMn04OlM/WDqKz0nqexlZmey+spqAPpV7YeaQo1H95K4EnQPgPpdXN7oLy5ehXcu0HmbBAfnbnk6ceIELi4ueR5En6pevTqXL1/O1bXsWW5ubmRlZXHmzBlVi0VYWBiPHz8usA5eXl4sW7aMuLi4fFt1tLS0VC1MBXF3d2fFihUkJyergoqgoCDU1NRytSq8jPzqERQURMeOHfn4448BUCqVhIeHU6VKlRfm5erqyj///JNr26lTp3K9LuxaF1Sn4p5DtWrVyM7OJiYmhoYNG+Z7XGHn6ejoiKamJqdOnVIFYQkJCYSHh9OoUaMil/Mis2bNYtq0aezevTtPS+NTJ06cyPO6qC1fiYmJtGrVCm1tbbZv355va9G0adOIiYlRjRvau3cvRkZGhb7fklQctxJv8Xvo70DOtMz6mi/3jb6BlgE93Xvy64Vf+S30N5pVavZKHjTWXllLeHw4xtrGfFnzy8IPKMCdq3GEBed0B63TwbG0qvfKOXhb0mqgB7t/u0h48APVqu8voq6hhp6xFgam2rjULIdbvfJoar++dXnMbPRzZmObf5YHkYn8veg8HYb7qOqgVAquHrtP8F83SEnI+WLLyt6IBl2ccwWgjtUscalVjmunHuR0YfuuJhqa6pjomPBDwx8YsGcA2yK2Uad8HTo4dXht5ye9frtu7iImJQYLXQvaObYD4PiWCITIuU/KO+UdZ/2ue3866b2Bbt++zRdffEFYWBjr1q3jp59+YuTIkQWmHzNmDMeOHWPYsGGEhIRw7do1tm3bxrBhw4CcB/jWrVszePBggoODOXPmDP7+/i9stenZsyfW1tb4+fkRFBTEjRs32Lx5M8ePHwdy1oqJjIwkJCSEhw8fkp6enieP3r17o6OjQ79+/bh48SIHDx5k+PDh9OnTJ8+4ipKyt7fnyJEjREVF8fBhTr9kFxcX9u7dy7Fjx7hy5QqDBw9Wfcv/IoMHD+bq1auMGTOG8PBwNmzYoJo04ekDSGHXuqA6FXYOSUlJ7N+/n4cPH5KSkkLlypXp3bs3ffv2ZcuWLURGRnLy5ElmzJjBjh07inSehoaG9OvXj6+++oqDBw9y6dIlBgwYgJqamup8ilJOQX744QfGjRvH8uXLsbe3Jzo6mujoaJKSknKlCwoKYtasWYSHh/Pzzz+zcePGF97PTyUmJtKyZUuSk5P5/fffSUxMVJXxNDBs2bIlVapUoU+fPpw/f57du3czduxYhg4dirb2q/3mVXp/CCGYHjydDGUG9W3q09KuZank+7H7x+hq6HL50WWO3z9eKnk+637SfX4O+RmAL2t8ialOycbU3Ax9yI5FFxACXGqVo5zD27UwoGO1nGDH1FoPU2s9bF1NcKlVDp/mFanf2Znmn1Sh4ygfek6og/+8hgz+yZe+0+rTeXQNPBtXeK1BzlMWFQz4cIQPWroa3L+ewI7FF8jKyOZm6EP+mHqSg2uukpKQgZGFDi39q9J1TI18W9kada+MrpEW8feTOfX3TdX2mtY1+czrMwCmnpjKrcRbr+nMpNdNCEHApZyJqXq790ZLXYu7V+O4dfERamoKVbfG985rmAHupRVneum3ha+vr/j888/FZ599JoyMjISpqan47rvvck0v/eyU0E+dPHlStGjRQhgYGAh9fX3h5eUlpk2bptp///590a5dO6GtrS0qVaokVq1aVej00jdv3hRdunQRRkZGQk9PT9SsWVMEBwcLIXKmJO7SpYswMTEplemlnzVy5Ejh6+tbpOt1/Phx4eXlJbS1tVVTOT969Eh07NhRGBgYCCsrKzF27FjRt2/fXOU8P8XyU89PL71kyRIB5LqPCrvW+dWpMJ999pkwNzfPNb10RkaGGD9+vLC3txeampqifPnyolOnTuLChQtFPs/8ppeuXbu2+Oabb1RpCivn6fTSz7Ozs8szNfaz9X+aZtKkSeKjjz5S1WHhwoVFuib5Tb399OfZ6a1v3rwp2rRpI3R1dYWFhYX48ssvVdNPF+Rt/XyQysauyF3CY4WHqL6quriVcKtU854ZPFN4rPAQn+z6pFTzFUKI4fuHC48VHqLvP31FtrJk0yxfP/NALP78wH/TNWeU/nTNUsHuRzwWv4w4JBYN3q+aAnjR4P3ity8Oi5B9t4v0fkScixGLBu8XP3+2X0Tf+O95KSs7S/Tf2V94rPAQH23/SKRnFTwl/5suIztDZGVnlXU13ki7I3cLjxUeotaaWuJx2mOhzFaK9VODxaLB+8Xh9WFlXb1SV9TppRVCCPF6Q6viS0xMxNjYWDXl7LPS0tKIjIzEwcEhT3eXN1njxo3x8fEp9mBt6dWYNm0aS5cu5c6dO2VdlVKRnJyMra0tc+fOZcCAAWVdnTLztn4+SK9fUkYSHf/sSExqDEO8h/C5z+elmn90cjRttrQhS5nF6jar8bHyKZV8D9w+wMiDI9FQaLCxw0acTQvubluQsOBo9q+8glAKXGpa0eyTKqUyTbNUPPeuPeavn0LIylCirqGGV5MKVG9th45+0SeV2PP7Ja6deoCptR7dvq+FhmZOK1V0cjQf/fURj9Mf06dKH76u9fWrOo1X5lr8NT7f/zlpWWn4OfvRtXJX7Izek0UvC3Hp4SU+2f0JqVmpfOLxCV/U+IKw4Gj2BVxGS0edj6fWQ9fg3Vos/UWxwbPkJ5n0Xlq8eDGnTp3ixo0brF69mtmzZ9OvX7+yrlaJnTt3jnXr1hEREcHZs2fp3bs3AB07dizjmknS22Hx+cXEpMZQ0bAiAzxL/8sBa31r1fTSy0KXlUqeKZkpzDg5A8gZeFySIOfyv/fYt+IyQilwq2dN809LZy0aqfhsXEzw+191ara1p9fEOtTv4lysIAee6cIWncKpvyNV2631rZnSYAoAqy+v5vi90u9C+SpdfHiRT3Z/QnRyNI/TH7Pi0grab22P/x5/dt/cTWZ2ZllXsczceXKHz/d/TmpWKvVt6jO82nCyMrI58WcEANVb271zQU5xyE8zqcwdPXoUAwODAn9ehWvXrtGxY0eqVKnClClT+PLLL196OuM2bdoUeA5P16l5lebMmYO3tzfNmzcnOTmZo0ePYmFh8crLLczatWsLvC5Vq1Yt6+pJEmFxYQReCQTguzrfoa3+asZ9ferxKWoKNQ7fPUxYXNhL57c4ZDHRydHYGtgy2HtwsY+/cPAOB9dcBQEevrY07eNe6LTM0qtVzsGIOh86YmRRshlRdQw0adwrZxKgc3tu8yDyvzWBGldsTHfX7gBMPj6Z1KzUl6/wa3A6+jT+e/xJSE/Ay8KLeY3n0ahCIxQoCL4fzOjDo2mxqQULzy7k7pO7ZV3d1yo+LZ4h+4YQlxaHm5kb8xrPQ1NNkwsH75IUn46BqTbeTUu2aPC7QnZdk8pcamoqUVFRBe5/0cxnb5KoqChSU/P/w2FmZvZSaxW9zZ48eVLgJBGamprY2b26rgfy80EqjFIo6buzL+djz9PCrgXzGs97peV9dfgrdt3cRRuHNsxqNKvwAwoQFhdG97+7ky2y+bnZzzSq0KhYx5/dfYvjW3O+8fVpXpH6XZzfu2ln32V7l18i/GTeLmzJmcl0/LMjD1Ie8EnVT/ii5hdlXNMX+zfqX0YdHEV6djq1rWvzY9MfVTMh3ku6x+Zrm9lybQsPU3MmBFKgoL5tfT6q/BG+FXzRUHt3JxdOzUrFf48/F2IvUF6/PGvbrsVSz5LUpAzWjD1ORlo2zfu741q3fFlX9ZUoatc1GehIkvTOkp8PUmG2XNvChGMT0NPQY7vfdsrpl85MkQW5GneVj/76CDWFGn/5/UUlo+Kvy6UUSvrs7MOF2AvFDs6EEJz8O5LTO24CULOdPbXbO8gg5x2TlpxJ4KRgUhMzqN6qEvU6/feF4eE7hxl2YBhqCjXWtVtHFfM3c4r+vbf28vWRr8lSZtGoQiPm+s5FRyPv53imMpPDdw6zIWxDrlkNrXSt6Fy5Mx9V/ggrPavXWfVXLluZzReHvuDAnQMYaRmxus1qHE1ypoM/+kc4Fw7exaKiAd2+rYXiHW2llWN0JEmSJOkF4tPimXcmJ0j43OfzVx7kALiZudHQtiFKoWT5xeUlymNT+CYuxF5AX1OfMbXGFPk4IQTHt0Sogpy6fo7U6eAog5x3kI5+7i5s0ZEJqn2+FX1pbd8apVAy8dhEspR5F08ta9uub2P04dFkKbNobd+aBU0W5BvkAGiqadLcrjm/tvyVHZ128InHJ5jpmBGTGsPS80tpv7U9Ky+tfCPPsySEEMw8OZMDdw6gpabFT01/UgU5jx+kcPFwTg+Z+l2c39kgpzhkoCNJkiS9lxacXUBCegIupi70cu9VYLqboQ85+XckF49EEXk+lphbiSTFp6PMVpao3IFeAwHYHrGdB8mFr/311JOMJ/x64Vfmnp4LwPBqw4scnAml4Oj6cM7tvQ3AB91cqNHavngVl94qjj6WVK5dDiHgwMorZGX+t2D1mNpjMNIy4krcFVZfXl2Gtcwr8EogY4PGohRKOrt0ZmbDmWiqFW1ShkpGlfiixhfs7bqXWY1m4WXhRWpWKnNOz6Hnjp5cfHjxFdf+1Qu4FMD6sPUoUDCj4Qyql6uu2nfizwiUSoGdhzkV3d7P7vLPe3c7L0qSJElSAc7FnGPLtS0AjKs7Lt8HqcyMbI6uD+fKsfv5Z6IAXUMt9I210DfWRt9YC73//7eCmxkm5fTyPayaVTVqlKvBmQdnWHV5FV/V+uqFdU3MSGTtlbWsvryaJxlPAKhZrqZqYHlhlErBoTVXc85DAY17uVK1oW2RjpXebg27V+bu1Xjio1M4+Vck9TvndGGz0LXgq1pfMS5oHD+H/EyzSs1K1I2ytC0LXcbCswuBnIV2v671dYlaHLXUtWjj0IZW9q348/qfzD09l6txV+m1oxc93HowotoIDLRezWRHr9KOGzuYf2Y+AF/V+oqW9v8tanzvWjwR52JRKKBe5/d0cdB8yBYdSZIk6b2SpcxiyomcqXY7OXeimlW1PGnio5PZNPM0V47dR6EA55pW2HtZYGVniL6Jdk6XEAGpiRk8vJPErYuPuBx0n9P/3OTwunDWTzlJ7J0nBdbB39MfgI3hG3mc9jjfNAnpCSw6t4hWm1qxOGQxTzKe4GDswMyGM1nWclmRBlorlYJ9AZdV59G8fxUZ5LxHdPQ1adw7pwtbyN7bRN/4rwtbR6eO1Clfh/TsdCafmExZDtkWQrDgzAJVkPOZ92clDnKepaZQo7NLZ7b7bae9Y3sEgnVX19Hxz47svbW3TM+5uILvBzM2aCwAfav0pU+VPqp9qUkZ7F1+GQD3D2wwt3n7grhXRbboSJIkSe+NzOxMfrnwC9fir2Gsbcz/avwvT5qw4GgOBYaRlZ6NrpEWLQdUpYKraa40SqUgLSmT5MfpJCekk5KQQXJCOskJGURHJPAoKom9v1+i23e10NBSz1NGA5sGuJu5cyXuCmuvrmWoz1DVvvi0eFZdXkXglUBSslIAcDZxZrD3YFpUaoG6Wt78CnJy+w2unXqAmpqCFgOq4lzj3RqULRXOwduSynXKER78gAOrrqhmYVMoFEyoO4FO2zsRfD+YbRHb8HP2e+31UwolM4JnsD5sPQBf1viS/h79S7UMc11zZjScwYdOHzL1xFRuP7nNF4e+wLeCL9/V+Q4bA5tSLa+0hceHM+rgKLKUWbSyb8WXNb9U7RNKwb7ll0mKT8eknB4NurwdM9W+LrJFRyqWFStWYGJi8sI0EydOxMfH57XU521UlGsoSVLpuhp3lZknZ9J0Y1N+ufALAP+r/j9Mdf4LYLIysjm45ir7Ai6TlZ6NrasJ3b+vlSfIAVBTU6BnpIVlJUPsPS2o8oENtdo50LiXKx1H+aD3/4s2HtsSkW99FAqFqlUn8EogyZnJPEp9xLwz82i1uRXLQpeRkpVCZdPKzGs8j80fbqa1fetiBTk3zsVyZtctAJr1d5dBznusYbfKqnvy5Pb/FhKtaFRRFWTPPjVbNU3z65KlzGJc0DjVmJPx9caXepDzrHo29dj84WYGeQ1CQ02Dw3cP47fN742erCA6OZoh+4aQlJlEjXI1mPbBNNQU/z2+n9l1i9uX49DQVKP1IA+0dGQbxrPk1ZCk16x79+60bdu2yOnt7e0ZNWoUo0aNenWVkqR3UHxaPDtu7ODP638SFv/fAp2Wupb0dOtJJ5dOqm2PH6Sw69eLPIpKAgXUbGtPrXYOJVpAU9dQi6b93Pn7p/OEHrpLpapm2HvmXby3WaVm2BvZczPxJp/v+5zLjy6Tlp0GgLuZO595f0bjio1zPdQU+dyjk9m3Mqcri3ezilSubV3sPKR3h46+Jo0/duOfxRcI2Xcb55pWWNnlTMnbp0ofdkbu5ErcFX44+QOzfWeXWrmZ2Zk8SnvEo9RHqn8fpj5U/T8yIZKw+DDUFepM+2Aa7RzblVrZBdHR0GF4teG0c2jHpOOTOBtzljmn5/D3jb8ZX3c8npaeudILIchQZpCamUpadhopWSmkZaWRmpVKelY6NgY22BnZvZLZCxMzEhmybwgxKTE4GjuysMnCXAsa3w2L5+RfNwBo1NMVc1vZZe15MtCR3itCCLKzs9HQKLtbX1dXF13dkq16/TIyMjLQ0tJ67eVK0uuUqcwkKCqIP6//yeG7h1Xf0mqqadKkYhP8nP2oZ1Mv1/iWa6cecHDNVTLTs9E11KTFp1Wp6P5yMxbZVTXHq0kFLhy8y4FVV+gxrg56Rrl//9TV1BngOYBxQeM4G3MWAA9zD4b4DKGhbcMSPzhlpGaxc2komWnZ2LiYyIHJEgAOXha41CrHtVMPOBwYRpcxNVFTU6ChpsHE+hPptaMXu27uor1je3wr+hY7/5TMFAIuBXA6+rQqkEnMSCz0OE01Teb4zqFppaYlOa0SczRxJKB1ANuub2PO6TlcjbtK739642jsSFp2TiCTlpVGWnYaSvHiGRbNdcypXq46NcrVoLpVdSqbVi5W6+uzhBA8SHlAxOMIloUu4/rj61jqWrKk+RKMtY1V6ZIT0tnz+yWEALf65XGv/24uDPqy3rmua0IIlCkpZfJT3EFtSqWSGTNm4ODggK6uLt7e3mzatAmAQ4cOoVAo2L9/PzVr1kRPT4/69esTFvbft5Lnz5+nSZMmGBoaYmRkRI0aNTh9+rRq/7///kvDhg3R1dWlYsWKjBgxguTkZNV+e3t7pk6dSt++fTEwMMDOzo7t27cTGxtLx44dMTAwwMvLK1eeT/3555+4uLigo6NDq1atuHPnzgvPddmyZbi7u6Ojo4ObmxuLFy8u8nW6e/cuPXv2xMzMDH19fWrWrElwcDA3b95ETU0tT/0WLFiAnZ0dSqVSdR137txJjRo10NbW5t9//6V///74+fnlOm7UqFE0btxY9XrTpk14enqiq6uLubk5zZs3z3X98rNnzx50dHR4/Phxru0jR46kadOcD/H8uq799ddf1KpVCx0dHSwsLOjUKeeb5saNG3Pr1i3+97//oVAocj34bN68mapVq6KtrY29vT1z587Nlae9vT1Tpkyhb9++GBkZMWjQoBfWXZLeZtfjrzPn1BxabGzB8APD2X97P1nKLKqYV+G7Ot9xsNtB5jaeS8MKDVVBTlZmNocCw9jz+yUy03OCgu7f137pIOepep2dMLPRJ/VJJgdWX8n3b0Q7x3Y0rtiYOtZ1WNJ8CYHtAmlUoVGJgxwhBPtXXiE+OgV9E21aDfRAXf2d+1MvlVCDrs5o6agTc+sJl/+9p9pexbwKfav0BWDKiSkkZ774b92zhBDsjNxJh60dWHp+KacfnCYyIVIV5GgoNLDSs8LdzJ0PbD+go1NHPvX4lK9rfc0PDX9gm9+21x7kPKWmUKOTSye2+22ng2MHBIKIhAiikqKIS4sjJSslV5CjqaaJkZYRVnpW2BvZ42zijJaaFo/SHrH31l5mnpxJt7+78cH6DxiybwjLQpdx9sFZMrIz8i3/Ueojgu8Hs/bKWiYdn0Sff/rQYF0DWmxqwWf7PuP0g9Poa+qzuPniXOOIlNlK9v5+idTEDMxt9WnUo/Irv1Zvq3euRUekphJWvUaZlO169gwKvfynE83PjBkzWLNmDUuXLsXFxYUjR47w8ccfY2lpqUrz/fffM3fuXCwtLfnss8/49NNPCQoKAqB3795Uq1aNJUuWoK6uTkhICJqaOVOkRkRE0Lp1a6ZOncry5cuJjY1l2LBhDBs2jICAAFX+8+fPZ/r06YwbN4758+fTp08f6tevz6effsrs2bMZM2YMffv25dKlS6o/vCkpKUybNo1Vq1ahpaXF559/To8ePVT1et7atWsZP348ixYtolq1apw7d46BAweir69Pv379XniNkpKS8PX1xdbWlu3bt2Ntbc3Zs2dRKpXY29vTvHlzAgICqFmzpuqYgIAA+vfvj5raf3/cv/nmG+bMmYOjoyOmpnn72z/v/v379OzZk1mzZtGpUyeePHnC0aNHCw1mmzVrhomJCZs3b2bAgAEAZGdn88cffzBt2rR8j9mxYwedOnXi+++/Z9WqVWRkZPDPP/8AsGXLFry9vRk0aBADBw5UHXPmzBm6devGxIkT6d69O8eOHePzzz/H3Nyc/v37q9LNmTOH8ePHM2HChELPWZLeRpnZmXz777fsvrlbtc1Mx4z2ju3p6NyRyqb5PwA8jklh928XeXgnp6tajdZ21G7vgFopBgUamuq0+LQqG2ee4lboIy4dicLDt0KuNJpqmvzU9KdSK/Ps7lvcCIlFTUNB68EeeVqRSpsQgqSDh9BysEfbweGVliW9PH1jbWp/6Mi/G65x4s8InKpZomuYc48M8RnC3lt7uZt0l4VnF/Jdne8Kze9a/DWmB0/n9IOcLxwrGFRgoNdAKhhUwFzXHAtdC4y0jN74RWnNdc2Z3nA6n3p8yqO0R+ho6KCroYuuui66mrroqOugo6GT70yHGdkZXHx4kbMxZzn94DQhMSEkZSbxb9S//Bv1LwBaalp4WnriY+lDcmYyEQkRXI+/Tnx6fL71UVeoY2dkh7OJM/2r9sfNzC3X/pN/RxIV/hhNbXVaDfRAM58JT6Qc71yg87ZIT09n+vTp7Nu3j3r16gHg6OjIv//+yy+//KL69n3atGn4+uY0IX/zzTe0a9eOtLQ0dHR0uH37Nl999RVubjm/AC4uLqr8Z8yYQe/evVXjOlxcXPjxxx/x9fVlyZIl6OjkrDDctm1bBg8eDMD48eNZsmQJtWrV4qOPPgJgzJgx1KtXjwcPHmBtndPHOzMzk0WLFlGnTh0AVq5cibu7OydPnqR27dp5znXChAnMnTuXzp07A+Dg4MDly5f55ZdfCg10AgMDiY2N5dSpU5iZ5XzL6uz834wi/v7+fPbZZ8ybNw9tbW3Onj1LaGgo27Zty5XP5MmTadGixQvLetb9+/fJysqic+fO2NnZAeDp6VnIUaCurk6PHj0IDAxUBTr79+/n8ePHdOnSJd9jpk2bRo8ePZg0aZJqm7e3NwBmZmaoq6tjaGiouv4A8+bNo1mzZowbNw6AypUrc/nyZWbPnp0r0GnatClffvnf7CyS9C7JVGby9ZGv2XdrH0aZZtSzakAzm+Z4mnojshRk31FyLeIBWRlKsjOzyfz/f9NTs7l0NIrMtGx0DDRp8UkVKlU1fyV1tKhgQP1Ozvy78Rr/brqOTWVTzMrrv5Kybl9+RPC2/++v370y1g7GhRzx8uIXz+bBTwGgAFNfVyxHjELdrSGUsNuO9Op5+tpy9fh9Ht5J4tjWCJr1dQdAV0OXCfUnMHDPQNZfXU9bh7b4WPnkm8eTjCcsDlnMuqvryBbZ6Kjr4O/pT3+P/rnGkLxtnE2dcaZ4s5ZpqWtRvVx1qperjr+nP9nKbMLjwzkbc5YzD85w5sEZ4tLiVP9/lgIFFQ0r4mTihLOJc86PqTP2RvZoqef/JcWti484szNnkpEmfdwwtX41nyfvincu0FHo6uJ69kzhCV9R2UV1/fp1UlJS8jx8Z2RkUK3af2s6eHl5qf5fvnxO/8uYmBgqVarEF198gb+/P6tXr6Z58+Z89NFHODnl9MU+f/48Fy5cYO3atarjhRAolUoiIyNxd3fPk3+5cjkrbD/7QP90W0xMjOpBW0NDg1q1aqnSuLm5YWJiwpUrV/IEOsnJyURERDBgwIBcLRJZWVkYGxf+RzgkJIRq1aqpgpzn+fn5MXToULZu3UqPHj1YsWIFTZo0wd7ePle6Z1t8isLb25tmzZrh6elJq1ataNmyJV27di1Sa1Dv3r2pW7cu9+7dw8bGhrVr19KuXbsCZ1oLCQnJdW2K4sqVK3Ts2DHXtgYNGrBgwQKys7NRV895yCjueUvS2yJLmcX3R7/n8I2jdAwbQfnEnM++G8ANzhcpj/LOxrQc4IGB6at9MPNqUoFbFx9y50o8e5dfouvXNVHXLN3uZIkPU1X99as0KP9a1srJvHuXmKUrcl4IiD8URuKxQVjWUGLSrhkKt1bg1BR0TV55XaSiU1NXw7enK5tnneHqsftUaWBDeaecv8d1y9elo1NHtkVsY8KxCWzssDHXA7dSKPkr4i/mnZlHXFocAC3sWjC65ug3form10VdTR13c3fczd3p7d4bIQS3Em9xNuYsF2IvYKRlhLOpM04mTjgaO6KrUfRnxydxaewLyJlkxMPXFpea5V7Vabwz3r1AR6EoVvexspKUlATkdFuytc39B0lbW5uIiJwpSZ92RQNUTb9KZU5/0YkTJ9KrVy927NjBzp07mTBhAuvXr6dTp04kJSUxePBgRowYkafsSpX+W/04v/xfVGZJz/O3335TtQA99fRh/EUKG7SvpaVF3759CQgIoHPnzgQGBrJw4cI86fT1c3/joaamlqcbWmZmZq667d27l2PHjrFnzx5++uknvv/+e4KDg3EopHtGrVq1cHJyYv369QwZMoStW7eyYsWKEp/jy3j+vCXpXZCtzGZ80Hj2Ruyn/dXPsX7igEIBmjoaaGiqoaGlhoaW+v//X/2/11pqaGjm/GtaTo8qH9iUale1gijUFDTrV4X1U07y8E4SwdtvUL8U17rIzMhm5y+hpCdnYWVnSMPX0F9fCEH0aH9EpkDXKhuLT3oSE/AX6TGpRB9XJ/7yfspV34J+eSVUqgcuLaFyK7CoDG94N6b3gbWjMe71y3Pl2H0Orwuj27c1Vb8Lo2uO5mjUUW4k3OD30N8Z4jMEgMuPLjM9eDrnY3O+SLA3sufb2t9S37Z+mZ3H20ChUGBvbI+9sT2dXTqXOJ/sbCV7ll0kLTkTy0qGfNDVpfCDpHcv0HlbVKlSBW1tbW7fvq3qmvasp4FOYSpXrkzlypX53//+R8+ePQkICKBTp05Ur16dy5cv5+rmVVqysrI4ffq0qvUmLCyMx48fq1qJnlWuXDlsbGy4ceMGvXv3LnZZXl5eLFu2jLi4uAJbdfz9/fHw8GDx4sWq7maFsbS05OLFi7m2PTvGCXI+nBo0aECDBg0YP348dnZ2bN26lS+++KLQ/Hv37s3atWupUKECampqtGtX8JSZXl5e7N+/n08++STf/VpaWmRnZ+fa5u7unmdMVFBQEJUrVy5SAClJbyulUDLp+CR2XttF26ufYf3EAS1dDTqO8lFNl/sm0jfRpkkfN3YuDeXcvttUqmpGBbeXn/RACMHhtWE8vJOErqEmrQd7oqH56j8DnmxZTVLILVATlP9qCNod/od+n3HEr1tH7MIFpCekcPugBYYVUrHyOYbWzaOwdxyY2oNLK6jqlxMAyaCnzNTr5MSNkFge3U0i9HAU3k0rAmCiY8K3tb/lqyNf8Wvor9QpX4d/Iv9hQ9gGBAJdDV2GeA/hY/eP0VTXLKSU0pOSmMHe5ZdQZgs8fG1xqmb5Wr6oeFMc3xpB9I1EtHQ1ciYZKeVW4XeVvEplxNDQkNGjR/O///2PlStXEhERwdmzZ/npp59YuXJlocenpqYybNgwDh06xK1btwgKCuLUqVOqYGPMmDEcO3aMYcOGERISwrVr19i2bRvDhg176bpramoyfPhwgoODOXPmDP3796du3br5js8BmDRpEjNmzODHH38kPDyc0NBQAgICmDdvXqFl9ezZE2tra/z8/AgKCuLGjRts3ryZ48ePq9K4u7tTt25dxowZQ8+ePYvUQtK0aVNOnz7NqlWruHbtGhMmTMgV+AQHBzN9+nROnz7N7du32bJlC7GxsfkGc/np3bs3Z8+eZdq0aXTt2hVt7YK7xkyYMIF169YxYcIErly5QmhoKD/88INqv729PUeOHCEqKoqHD3MWc/vyyy/Zv38/U6ZMITw8nJUrV7Jo0SJGjx5dpPpJ0ttICMG0E9PYHv4XrcMGYpPojKaOOh1GeL/RQc5Tjj6WVPnABgTsW3GFtOTMwg8qROihKMKCo1EooKW/B4ZmOqVQ0xfLTkjgwYxZAJjXs0C7/SgAFBoamPXpg9PefZj27g3q6jy5q8uN3TbE3PZEqdSC+Jtw8hcIaAM/+sChmRAXWWBZ0quja6hFXb+cLp8nt98gOSFdta+VfSt8K/iSpcyi365+/BH2BwJBG4c2/OX3F594fPJag5yE2FQ2zz7D3avx3Lv2mD3LLrF67HHO7rlFesrL/x696W6ci+X8vpzZbZv1c8fY8vUvUfG2koFOGZoyZQrjxo1jxowZuLu707p1a3bs2FFo1yjI6Vr16NEj+vbtS+XKlenWrRtt2rRRDWj38vLi8OHDhIeH07BhQ6pVq8b48eOxsXn5PrR6enqMGTOGXr160aBBAwwMDPjjjz8KTO/v78+yZcsICAjA09MTX19fVqxYUaTz1NLSYs+ePVhZWdG2bVs8PT2ZOXNmnlaLAQMGkJGRwaefflqkc2jVqhXjxo3j66+/platWjx58oS+ffuq9hsZGXHkyBHatm1L5cqVGTt2LHPnzqVNmzZFyt/Z2ZnatWtz4cKFQluyGjduzMaNG9m+fTs+Pj40bdqUkydPqvZPnjyZmzdv4uTkpJqRr3r16mzYsIH169fj4eHB+PHjmTx5cq6JCCTpXSKE4IdTP7Dp6mZahn1KhQRXNLTV6TDM+7UMui8tH3zkgrGVLsmP0zm09mqxlyV41r3rjwnaeA2Aep2dqeBa+BjC0hD7/WdkJWWjaZiNxdTleVplNExNsR43FoetW9CrVxeRmc2jY4+IOOhGQrkvEV69QMswJ+g5NCMn4FneBs6ugrSE13IOr038TbiwEbLSC01aFqp8YIOVnSEZadkc23xdtV2hUDC27lj0NHKGAjibOLO81XJmNZpFOf3XOy7k4d0nbJl9hsTYVIwsdKje2g5dQ02S4tM5viWCFd8e48i6MB4/SHmt9XpdEmJT2b/qCgA+zSvi6GNZyBHSsxTiZT5lX5PExESMjY1JSEjAyCj3t3ZpaWlERkbi4OCgmklMev9MmTKFjRs3cuHChbKuivQGkZ8P7wYhBHNPz2X1xTW0CP8Eh3hPNDTVaD/MG9vX9HBfmh7cTGTLrDMolYKmfd1LtNBf8uN0Nkw/RUpiBs41rWg5oOprmcI39d893PQfASio9HVn9D/Nf9r8p4QQJB04wIOZP5D5/+ut6Xh7YTlkEPomsSgurIcbh4D/fxTR0AG39uDTExybvL2zt6UlwtE5cGIJZGdA5TbQfTW8xlaQooq5lcjGmadBgN//quX6nbry6Aq3ntyieaXm+U6t/LzEh6lkpGVhUcGwVOp271o8O36+QEZaNua2BnQY4Y2+sTZZmdmEn3zAhQN3eBT1/2v+KMDewxzvZhWxdTV946e0LoqszGy2zD5L7O0nWDsa4fdldbku1v97UWzwLBnoSG+1pKQkbt68SbNmzZg6dWqxZy+T3m3y8+HtJ4Tgx3M/8vuF5TQP74dTnA/qGmq0G+pVagt7loXTO28SvO0GmtrqdB9bC2PLok+ik52l5M9554i+kYCZjT5dx9REU/vVBwQiI4PI5rVJj0nHuKo+NhtPglrRHrqUGRnErVjJw6VLESk537xrVqqEafduGLeoj8btXRCyDh7+tyg2Btbg1Q18eoFV0boNlzllNpxbDQemQnLs/29UAAKqdoYuy97I4O1wYBgXj0RhWl6f7mNrFfthWgjBpSNR/LvxOtlZSirXKUeDLi4vtY7TjZBY9iy7RHaWEhsXE9oO8URbL3egKITgblg8F/bf4WboI9V2c1sDvJtVwKVWudcyZu1VObwujIuHo9DR16Tb97VeS9fUt4UMdKS3wvTp05k+fXq++xo2bMjOnTtfeHz//v1Zt24dfn5+BAYGvpaB+AYGBgXu27lzJw0bNnzldZCKRn4+vP2WhCxhScgSml7rg8ujGqhpKGg7xAu7V7TuzeuiVAr+nHeW+9cTKOdgROfR1fMdWJ2ZkU1aUiapTzJy/k3K5OaFh1w/E4OWrgYffVMTk3KvZ6bRR1OGE7N2H+raShw3rkGjcq3CD3pOZkwMj379jYQ//0T5/7NyKjQ1MWzdGtPu3dC1BsWFPyB0I6Q+s5hi9b7Qbt4b2SKiEnkEdn0HD0JzXps7k1ltNKnX72AYOQuFIhOq9YEPfyq1SRiEECgTE1EvwnINL5KWnEngxBOkPsmkXmcnqre0K/Kx6alZHFx9hYizsbm2a+lqUM/PkSoNbVFTK975Xg66x6E1VxECHLwtaDmgKhqFLIr5+EEKFw7c4crx+2Rl5MwUq2uoSbUWdng3r1jsOpS1kH23Cdp0HRTQfpj3W/+ZV9pkoCO9FeLi4oiLi8t3n66ubp6pt98E169fL3Cfra3tK50uWioe+fnwdlsWuoyFZ36kSUQvXGNro6amoPVnnjh4WZR11UpF4qNU/ph6iozULOw8zdHW0yDtSU4wk5qUE9g8fWDLT7vPvbB/TdciI+w8Nzp3R2QrKN+/ESbf/PJS+SlTUkj85x/i160n7dIl1XZtFxdMenTHuG1r1KOP5bTyhO8EoQTn5vDRStAu+MumMhF3A/aMg6t/57zWMSbN0Z9HwQkk7toN2dnoVXHA1u0EGlrZUPdzaDX9pYOdzKgo7gwbTsaNG1T87Vf0C5gQqKiuHLvPgVVX0NBWp/fEOhiYFv6Z+eBmInuWXSTxYRpqagrqdXaivJMJh9eFEXv7CQBWdoY07u2GZaXCu7MJITi35zbHt+bMPOtevzyNe7sWa3a1tORMLgfdI/TgXZLic8ZGlXc2pnn/KhhZvPl/n4UQBG+/oVoUtHYHB2q1K3xM8+uQkpFFtlJgqFP2XzjIQEeSpPee/Hx4e628tJI5p+bie6Mb7jH1UagpaOVfFafqVnkTx92A2PCcB2H1t2vVhPBT0ez9/fIL06hpKNDV10THUAtdA010DTRxrlEOx2qvZ1CyEII7HeqRfD0BvQoaVNp1BoVGTpekJ2mZ/Hwwglr2pjRzL9kg9dTQi8T/sZ7Ev3cg0tIAUOjpYdyuHSY9uqOrcRs2fgJZqVDeG3ptBMM3YKHEtAQ4MgeCl0J2BgJ1Us39eBgCyUH/zQyq0NREZGaiaW1GxepX0DbKhkZfQ9PvS1x0ytmz3B06lOz4xwBoVbLF4e9/UNMqeVcxoRRsmXOW6BsJOFW3pPUgz4LTCsGFA3c5tuU6ymyBobkOrfw9KOeQ84ymVAouHr5L8LYbZKRlo1CAR+MK1PnQEW3d/H9HhVIQtOW6anax6q3sqOvnWOKxNtnZSsJORPPvxmtkpmWjpaNOo56uuNaxLlF+r4NQCo7+EU7o4SgA6vo5Ur2V3SsZb5QeEUHsggWom5ig5eCIloM92o6OaNraotDQIDk9i0v3EgmNSuBSVAKhUQlExCbxbRt3BjZyLPX6FJcMdCRJeu/Jz4e309ora5kZPJMPIrvi8aAhCgW0+LQqLrXyebi9dw5WdoT0BDB1AN+vwbNb6QY8QuS0KLyisRWXg+7xODoFHUNNdA1ygpln/6+po16mA6sTfp3CvXmBKNQEDivmo137v9knv950ng2n7wLwbRs3BjV6iQfTxEQStm0n/o/1ZFz/by05HU9PrAd2QTfkO0h5CCaV4OMtYFFGCyYqs3NmiDswFVIeIgQkKWvx6IoBqRf/f4yRmhqGrVpiPsAfNW0t7nw2hMyoKNT0tKlQJwr9chnQYjI0GFns4h9v3sz9CRMgKxtt0wyyUtXJTlPHYvgwLIcOfalTe3j3CRumnUII6DDCm0pV8naXSkvOZP/KK9y8kLPcgWM1S5r2ccszfgYgOSGdoE3XuXbqAQB6Rlp88JELzjWtct0n2dlKDq66SlhwNAANujrj07xSnvxKIvFhKnuXXyb6Rs6Mfi61yuHbs3K+9S1L2dlKDqy8QvjJB6AA3x6V8fCt8GrKSkwksutHZN6+nXefmjoxRlbc0DXnjqEVUQaW3DGw4q6BFclauvSqU4npnQoOgl8XGehIkvTek58Pb5/AK4HMCJ5B/Vud8LrfGBTQvJ87rnXzmZns/gVY2QHSHoNCLScYgdILeB5FwLk1cH4dpDyCch5gU+2/H0u3t64FqbiyH9wionUrslMVWLTzwHLuRtW+o9di6fP7yVzp+9WzY3yHqqi/xHgIIQSpp08Tv/4PEvfsgcxM1PT1qfTjNHTPfJPTgqdrCj3XQ6W6JS6nRO6chL//Bw8uIpSQ8MieR+FmZNzJeUBXaGlh3KkT5p9+gpbdf+Ncsh494u7QYaSGhIC6Auvq8Zg6peSMO6o1oEhFi+xsYmZMI27NOgAMK6Zi09aSpItRRAUZo9DUwPGvv9Cyt3+pUzy6IZwLB+5ibKVLz3F1ci1MeT8igT3LLpIUn46ahoIPurrg4WtbaHB750och9eFkRCTCkAFN1N8e7piUk6PzPRsdv92kVsXH6GmpqBpX7f8f99fgjJbyZldtzi14yZCKTAw06Z5/yrYVn4zZm3Mysi5BjdDc65Bs0/cqVzr1bQ8CSG4O3w4Sfv288TYgqOOtdGPiaLCk1gqJMWgrcwq8FiFmTnmvXu+dEBdGmSgI0nSe09+PrxdAq8EMuPkDGreaUPNu60BaNLHjSoN8ln/68ElWNEeUuOgQi3ovgYu/AFBC3OCEihZwJORAle2w9nVcOvfF6fV0IXyXrmDH3PnN3JWrZK637cZj0/eQ8tUgcO+E6jp5/wNTk7PouX8I0Q9TqVfPTsqmukxdUfOWh8tq5Tjx57V0CmF2a6yHj0i6n9fkHLyJOqmptj99iPax7+GqDOgrp0zi1mVD1+6nMIrkg4Hp8OxH1FmCh7fNufRdTOy4nImVFAzMMC0Z0/M+vZBwzL/LoXK9HTuf/c9iTt2AGDmloSV1xMUXX4B7+4vLD77yROiPutH8pmca2zh8QSLgZ+iaPY9Yu8E7vzwB8nROujXq0vF5ctfqgUwPTWLwAknSEnMoM6HDtRs64BQCs7tvc2JbTcQSoGxpS6tBnoUadzNU1mZ2Zzbc5szO2+RnaVETUNB9ZZ23L0aR/SNRDQ01Wg1yAN7z1c37iw6MoG9yy+TGJsKCqje0o7aHRxQ1yi7KZszUrPYsfgC9649Rl1Tjdav+Bo8+v13YmbPIVNNg9ENPyfcNKflzNZEFw8bA2rqZFIlO56KSbFo3rtNRuRNMm7cICsmBgCLYcOwHCYDnVIlAx1JkkpCfj68PZ4GOZXiq9D26mAAfHsW0HUj5iqsaJfTjcmmOpmtfiXj/iP0atVCkZUKp38vXsAjRE4XuHOrIXQTpCfmbFeogVMzqN4npzXn/vmcdPfOwb0QyHiSNy8tAyjvAx6di/xN/ZsqZfuv3Pp6PgB2c79Fr93/sXff4VEVXQCHf9uy6b2HJBB6770r1QIIFhBpiqiIDfWzCxbELoqNoggCiiKgIKCA9N5CS4CQnpDe69b7/bEQiAkSIKGe93n2SXJ3Zu7cJWz23Jk5c25T5Sm/H2X+zniC3B34+7keOOm1rDp8mslLDmG0WGkT4s7cMe3xdLr8NSNnWQqLSBg3jtIjR9D6+RH6w1zsdr5uS1KACgZ+AB0fu+LzXFDKYVj+GKRHkBfrQNphHywltrveGh9vvMaMwf2BB9C4VG2xfeZXX5P55ZcAOAeVENS5APXIH6Dx3ZXWMZ48RuL4MRjTi1BprATersf1hTkQ0tFWoCQH47ttiVlhh2JREfjRR7jdfdcVXfLJPams+z4CjU7NPZPbsGdVDAnHbImD6rf3o9fIhtjZX95oZm56MVt/PklCxLlERHpHLXdNaol/WM1v/mssNbPtlygid6QA4BPiQt+Hm+Dh73TJbSmKckVBZUmBkZUzD5GRUICdvYY7n2xJYH33y27vYor27CFh3MNgsTCz5VAOtb6dafc0o3mQG17O+v+saykswhgbi8bDA7ta1z5RlAQ6Qohbnrw/3Bh+Ov4T7+1+DyeDOw8dewOVQUvz3rXo8UCDioUzo2DeHVCUDgEtKe34MQmPP4MlNxddrVp4jnoIt2HDbFmILxbwFGfb0hgfWABpR8+dwz3Elga41YPgdoE58lYrZEfbgp7kA7avKYdsC+bPGjrHtg/MDUgpzCGmbxeMOeDeMYSA+X+VPbcnNpv7Z9kW2y98pCPd6p+7+7w7JotHF+wjv9RMHW8n5o/rQIjXlae/NufkED9qFMZT0ehCQ6g9fz7afR/Avu9tBbo8DX3eqvK+PlViMcP2z2DT+yhmM+mRfmQfsY1S6UJD8HrkEdwGD0at/+8PiJXJW/UnKa++imI0onc3EdwrH934xbaEGucpWj6HpKmfYjWA1sFCrSd64zD2M7D712u6dy6ZH04h44grGk8P6q5Zc0UppxVF4fcZB0k+kVt2TKNT0+OBBjTuGnDFa8YURSH6QAbbl0ahUqu488kWeAVe3Wx60QfT2bjwOIYiM1qdmq731adp98AK16YoCiUFJnLTi8lNK//IyyzBzduBum19qdfW95KuoSC7lJVfhJOTWoyDi467n2p1SSNkl8qUnk7sPUOxZGWxoVYbZncdxbInu1LPt+bOWZNqNND56quv+Oijj0hNTaVly5bMnDmTDlVIa/jzzz8zYsQIBg8ezIoVK6p8Pgl0rh8//PADzz77LLm5uRcsM3XqVFasWEF4ePhV65cQlZH3h+vf2SBHbVUzPu5d1GlO+IS4MOzFtuXWBgC2NTM/3AkFKeDXnJIOH5E48TkseXm2VL1n/pypnZxwv3cYHqNGYefjXnnAE9ACTqwFiy39LBq97Y56m1FQu8flfWC2mCHzJOyfB3tmg50LPLENPGpf9utzrWROvoeM1cfROEDdv9aj8bXdwS01WRj4+VZiM4t4oF0wH9zbokLdqLQCxs7bS3JuCd7Odnw3pj0tg92vuE+mtDTiHxyJKTkZfcOGhM7/Ac2R72DD27YCzYbBkG9Ae+mBRwWZUbZRnOT9WEwqTh9uSGGUbaTP64nH8Zk0CdUV7ttWfOAgSZMmYcnOtgUxvYpweOYXqN0VTCVkTx1L2rJDoKiw94Van32Irm3loz5YzFi/6k7swgyM+Trchz9AwNSpV9S/7NNFLHl3D1argoe/I/0fbYZXUPUGI4pVQVGUS0ofXZ0KcwxsmB9B0nHbnk21m3vRoKM/eenF5KaVkJNWTF56MYbiC69bOZ9HgBP12vhQr60fnoEXHiHKTSvm988PUphtwNlDz6BnWl3WiFJVKWYz8WPHUbJvH7Gu/rzU+xnmPtadjmE37t48NRboLFmyhNGjR/Ptt9/SsWNHZsyYwa+//sqJEyfw9a0k7ecZcXFxdOvWjbCwMDw9PSXQuUFJoHNxY8eOJTc395J+x0XNkPeH69vZIAfg0eLX0Bzyxc5ew/2vdcDN51/7XWTH2oKc/GTwaUxJ+49ImPQC1vx8HFq1otbMLyjY8A/ZCxZgjImx1VGrcbn9NjzHjMGhWUNU+78vH/AA+DW3bUbZ/F5w9Ky0n5bCQlRqNWrHKo5MWMy2vibuglodYNyaGyppgXHnCmIeeRnFqiLwhTG4jX+57LnpqyOZtSUGP1c9fz/XEzeHyjNXpeWXMm7eXiJS8nHQafhqZGtua3TlKaGNCQnEjRyJJSMTh1atCPn+O9RRf8DvT4LVDLW729ZrObhf3gmsVtgzC9ZPBXMpRqM7SXtCMSRloNLrCZz+Hq533HHF11F2PUnJJD3+GIZT0bZpad0MuDwyldQPPiH3mO3DtVubAPy/WYLa7SLpxGM2U/TxvST84w0qFbV/WoxDq1ZX1L/YQxlknS6i5W3B6PQ3z9qz8ylWhUP/JLJzRTRW8wU+EqvAxcMed39H3H0dcfdzxN3PAVcvB9Ji8zh1IIOEY1lYLefqewY6Ua+tL3Xb+OIZcC6IyUgsYOUX4ZQUmHD3c2TQM61w8azZv09pH31E9nffU6zV83SvZ3np0X4MalnJ2scbSI0FOh07dqR9+/Z8eWZ+qdVqJTg4mKeeeoqXX3650joWi4UePXrw8MMPs3Xr1ot+CDQYDBgMhnIXExwcLIHOdeBGD3QURcFisaDV1tyHjhst0DEajdhdwd4L1zN5f7h+nR/kPOL6NLq/6gIwYEKzinvl5CbYpqvlJYJ3A0rafkjCU//DWliIQ9u2BM+ahcbZ9kFCsVop2r6d7B/mU7R9e1kT9k2b4jl2DK69u6E6+jMUpELTIbb1NGemqlhLSjBEx2CIisJwKsr2NeoU5pQUVDodLn374n7fvTh27IjqYiM+OfHwbTfbep+eL0PvV6rldatpirGEhDvaU5xkwam+B8F/bC+bynMoMZd7vt6OVYG5o9vRp8l/By6FBjNPLNzP1qhM1Cp4d0hzHux45SmDS0+eJH7UaKx5eTh16UKtb79BnbgdloyyrZvyaQzdnwffxrYU1FUd4clNgBUTIW4rAEWajiSvysOSl4/Wx4daX3+FQ/PqT6trKSgg+dlnyvbesXM1YczXAQq+Ywfj+dL7F5wq9su+RPbFZfPKwMZ4ONnBzyM5PX8bebGO6Bs2pM7SX1Hprq80yterzKRCdiw7hbHEfCaQsQU1Hv6OuPk4oLX770DPUGwi9nAmp/ankxiRXWnQ4+HvxMaFxzGWmPEOdubup1rh6Fqzf3/z160j+amnAZjWfhTdxz/AYz1t77eU5oPe5Yo3r70WqhroXNJYodFoZP/+/fTpc24OqVqtpk+fPuzcufOC9d5++218fX155JGqLcycPn06bm5uZY/g4OAq91FRFEwGyzV5XOosQKvVyvTp06lTpw4ODg60bNmSpUuXArBp0yZUKhUbNmygXbt2ODo60qVLF06cOFFW/9ChQ/Tu3RsXFxdcXV1p27Yt+/btK3t+27ZtdO/eHQcHB4KDg3n66acpKioqe7527dq8++67jB49GmdnZ0JDQ/njjz/IyMhg8ODBODs706JFi3JtnrVixQrq16+Pvb09/fv3JzEx8T+vde7cuTRu3Bh7e3saNWrE119/XeXXKSkpiREjRuDp6YmTkxPt2rVj9+7dxMXFoVarK/RvxowZhIaGYrVay17HNWvW0LZtW/R6Pdu2bWPs2LEMGTKkXL1nn32WXr16lf28dOlSmjdvjoODA15eXvTp06fc61eZqVOnMn/+fH7//XdUKhUqlYpNmzYBkJiYyP3334+7uzuenp4MHjyYuLi4srp79+6lb9++eHt74+bmRs+ePTlw4EC59o8fP063bt2wt7enSZMmrF+/HpVKVS6outh5zl77tGnTCAwMpGHDhhf9NxCiOp0f5IyrPQGXbY0AaN6rVsUgJy/Jll0tLxG86lHc6j0SJr2ItbAQx/btCZl9LsgBUKnVOHfvTsh3cwlb+Qfu992HSq+n9NgxTr/4P07dMYTMcDUlAfeTd+A06TM+J3Hik5zq158TbdoSd++9pLzyCtnffU/Rlq2YU2wLlhWTifzVq0kY9zDR/fqT+e23mNLSLnyRHqFwl20hP1s+hPgL/428nuR+MJHiJAsqjYL/J7PKPmAbzBZeXHoIqwKDWwVeNMgBcNZr+X5se+5tWwurAq8uP8Inf5+45L+V/2bfoAEhs2ehcnSkaMcOTj//Akpod3h4DbgEQEYkLBsP33aFaQHwZXtbELTxPTi23JbMwmI616Ci2LLsfd3FFuToHMlxHEPCkhQsefnYN2tG7aW/1kiQA6BxcSF41mw8HrgPAGO+DrWdhlqffYjXyx9UGuQoisKHa4/zv6WH+WVfEi8uPWx7Xfu9g2+bEjR2VgwnTpD948Ia6fPNyLuWM4OebsW9L7Wjz9gmtBtY27buJsj5okEOgN5RR6NOAdz1ZEse/qgbt49pTGgzL9QaFdmni9izMpa/5hzFWGImoJ4bQya3qfEgxxgXR9JLtpssv9XtQdi9g5hwdrPPvd/B+8EwpzdErrSNZt6ELum2dmZmJhaLBT+/8m9wfn5+HD9+vNI627Zt47vvvruku/uvvPIKkydPLvv57IhOVZiNVmY/s7nK56pOEz7veUlDu9OnT2fhwoV8++231K9fny1btvDQQw/hc15qytdee41PPvkEHx8fHn/8cR5++GG2n7lLOXLkSFq3bs0333yDRqMhPDwc3Zk7N9HR0QwYMIB3332X77//noyMDCZNmsSkSZOYN29eWfufffYZ7733Hm+88QafffYZo0aNokuXLjz88MN89NFHvPTSS4wePZpjx46VvdkWFxczbdo0FixYgJ2dHRMnTmT48OFl/fq3RYsW8eabb/Lll1/SunVrDh48yKOPPoqTkxNjxoz5z9eosLCQnj17EhQUxB9//IG/vz8HDhzAarVSu3Zt+vTpw7x582jXrl1ZnXnz5jF27FjU591xffnll/n4448JCwvDw+PiefNTUlIYMWIEH374Iffccw8FBQVs3br1on+gX3jhBSIjI8nPzy97nT09PTGZTPTv35/OnTuzdetWtFot7777LgMGDODw4cPY2dlRUFDAmDFjmDlzJoqi8Mknn3DHHXcQFRWFi4sLFouFIUOGEBISwu7duykoKOD5558vd/6qnAdgw4YNuLq6sm7duou+FkJUp5+P/3wuyGkyjtBNXUgtysMnxIWuw+qVL5x/2rZPTm48eNShuPk0Ep5+GaW4GMdOnQj++qv/nE6mr1+fgHfexmfyc+QuWULOosWY09PJmDGDjBkzKq2j8fBAX7/+mUc929d69TAmJZG7dCn5K1dhSkoiY8bnZHwxE+cePXC/716ce/SoeOe8+b1war1tH55lE+DxrZc/peoqMB7aQvqSHYAan4fuwK7BuQ/2X22M5mRaIV5Odky5u2mV29Rp1Hx0bwsC3R34YkMUM/85RXJuCe8PbYHdFaT0dWjZkuCvviRxwmMUrFtHyptTCHj3HVTj18O2GZB62BbQGPJs66YyT9rShp+l1tlGe3wbQ0kORP8DgBLUgbSE1uT8uBIA1zsGEjBtGmoHh0p6UX1UWi3+b72NvnFjCjf8jc+Lr2DfoJJkHIDFqvD6iiP8tMd2g1GjVrE+Mo3FexIY2TEMba/H8U2cTcoeDzJmfoHrgP7oAm/saUo3Gr2jjkadA2jUOYDSIhOxh2wjPUmR2YQ296LvI03RVSF4uhLWkhKin3waVXERR73qkHjvOL6+u6nts1zaMVh7ZpT59EFY8pBtX7Buk21r3W6gqbYXU6NXUlBQwKhRo5gzZw7e3lXPCa7X69FfRhaTG4nBYOC9995j/fr1dO7cGYCwsDC2bdvGrFmzmDBhAgDTpk2jZ8+egO3D+p133klpaSn29vYkJCTw4osv0qiR7W5o/frndomePn06I0eO5Nlnny177osvvqBnz5588803ZdN47rjjDh57zJaW88033+Sbb76hffv23Hef7c7SSy+9ROfOnUlLS8Pf37Z5lclk4ssvv6RjR1tqy/nz59O4cWP27NlTaVKKKVOm8MknnzB06FAA6tSpQ0REBLNmzbpooLN48WIyMjLYu3cvnp62+fP16p37MDR+/Hgef/xxPv30U/R6PQcOHODIkSP8/vvv5dp5++236du373+e63wpKSmYzWaGDh1K6JlN35pX4W6es7MzDg4OGAyGstcLYOHChVitVubOnVsWMM6bNw93d3c2bdpEv379uO2228q1NXv2bNzd3dm8eTN33XUX69atIzo6mk2bNpW1PW3atHLXtWTJkoueB8DJyYm5c+fetFPWxPXp5+M/M233NADGNR1Hp8RBHIxOwM5eQ/9Hm5ZPPlCQZgtysmPAPYSipm+T+NyrKCUltulKX31Z5Q+fWg8PvB9/HK+HHyZ/7Vqyf1yIKTERu7CwskDmbGCj9ap8ca6DmxsOTZvi97//kf/XX+QuXUrJvv0UbtpE4aZNaHy8cR8yBPdhw8pv2HjHR5CwE3Li4M/nbfu+XIfTRBSTidPPP43VrMYxxAnPFz8sey7idD5fbzwFwFuDm15yymiVSsXkvg0IdLPntRVHWXYgmQPxOTzSrQ7D2tbC0e7yPoo4de5M0GefkvTMs+QtW4bGxRnfl19GdefHZy5KsSWuSI+wBT3pkbbRnowTYCw8czzCVlZjh6XjCyQvOUnRdluQ4/30U3g/8cQVZxi7FB7DR+AxfMQFny81WXj253DWHkstmw5YbDTz7p+RvLMqgo51PKnX/QXcDv5EbqyBkgxIfXcawV9/ddWuQZRn76SjcZcAGncJwGKxolaravx3SlEU4l6fAtFR5Oid+WPQk8we2d62ka+pFH571JaIpV4f2/TdPXMg4zgsnwAbp0G3Z6Hlg6C78ad8X9K7i7e3NxqNhrR/Ddef/yH4fNHR0cTFxXH33eeyhFjPDI1ptVpOnDhB3bp1L6ffF6S1UzPh857V2ualnLuqTp06RXFxcYUP30ajkdatW5f93KLFuYw2AQG2nYLT09MJCQlh8uTJjB8/nh9//JE+ffpw3333lb2ehw4d4vDhwyxatKisvqIoWK1WYmNjady4cYX2z47Unf+B/uyx9PT0sn9jrVZL+/bty8o0atQId3d3IiMjKwQ6RUVFREdH88gjj/Doo4+WHTebzbhVIfVleHg4rVu3Lgty/m3IkCE8+eSTLF++nOHDh/PDDz/Qu3dvav9rZ+jzR3yqomXLltx+++00b96c/v37069fP+69994qjQZV5tChQ5w6dQqXf+2zUFpaSnR0NGD7f/T666+zadMm0tPTsVgsFBcXk5CQAMCJEycIDg4u93/t3693Vc4Dtn9jCXLE1fTvIGeofjR//nUYgN6jGuPmc97ITGGGLcjJOgVuwRQ2fpuk599EMRhw6tGdWjNnXlZKX5WdHW6DBuE26PI3mFQ7ONgCmiFDMMTEkvvbUvJW/I4lI5OsOXPJmjMXx/bt8Rw3FpfbbrPNfx/2HXzXD44uhfp9oeXwyz5/Tcme/gwlSQbUWoWAGV+jOrOO0Wyx8r/fDmG2KvRv6sedzS9/x/rhHULwc7XnuV/Cicsq5o3fj/HJupOM7BjCmM618XW99A9VLn36EDDtXVJefoXs+QtQu7qe27VdpQLXQNvj/LTNVqttKmTGmeCnOBODZy+S3vgUY2wsKgcHAt9/H9f+/S77WsE23W9HdBZJ2cUMaBaAj8uV3cAtKDUxYcF+dsZkYadR8/nwVgxsHoDVqrD5ZAZbozJ5+qdwlj/ZBX2fNwhIeY6YtT4U/vMPBRs24HL77Vd0fnHlNFcpu1z6T0sw/LkSCyrm3fYIM57sg8PZEaR/3oH0Y+DobctS6OwLXZ+GvXNh59e2EfRVz8GmD6DLU9B2LOivburv6nRJgY6dnR1t27Zlw4YNZesbrFYrGzZsYNKkSRXKN2rUiCNHjpQ79vrrr1NQUMDnn39+SWtvqkqlUt0QmUEKC227Kf/5558EBZXfeEmv15d9KNWdNxXi7B2As8Hi1KlTefDBB/nzzz9Zs2YNU6ZM4eeff+aee+6hsLCQxx57jKeffrrCuUNCzi0Graz9/zrn5V7nnDlzykaAztJUITWnw0Xu2NrZ2TF69GjmzZvH0KFDWbx4MZ9//nmFck5O5dM2qtXqCtPQTKZzc7Y1Gg3r1q1jx44d/P3338ycOZPXXnuN3bt3U6dOnYv2+98KCwtp27ZtucDzrLNTFceMGUNWVhaff/45oaGh6PV6OnfujNForNbzQMXXQ4iatOLUinJBzqNhT/DLNNvauua9alGv7Xnrciwm+Gk4ZJ4Al0AKG75J0otTUYxGnHv1IuiLz1FfJ0G6PqwOfi++iO8zz1CwaRO5v/1G0dZtFO/dS/HevfhPnYrH8AegVjvo9QpsfBf+fAGCO4Lnpb+P1JTS8F1k/PwPoML3oduwa3LuBsrsrTEcTc7H1V7LO4ObXfGd6N6NfNn+0m0s3Z/Ed9tiScgu5quN0czZEsugVoE80q0OjQMuvLC4Mu5DhmDNLyDtvffInPklGhdXPEePunAFtdq2hsojFBr0p2jHDpImPoc1Px9tQADBX32JfZMml3V9xUYzm09ksPZYKv9EplNgsGVO+2DtCZ7oVZdHutXBXnfpn1EyCw2MnbeHo8n5ONlpmDO6HV3qeZ+5HBUf39eSATO2EJGSzyd/n+TVASPR75mDV1wsWZEupL47DadOnVDLe/9Nr/DwEdKnvYcW+LXVXbz22qhzm4FGb4SdtmRiDP7KFuQA2LvZEnh0fMK2r9iOL2wZLv9+DbZ+Ap2egA6PgsPl3ey9li55vHjy5MmMGTOGdu3a0aFDB2bMmEFRURHjxo0DYPTo0QQFBTF9+nTs7e1p1qxZufru7u4AFY7fapo0aYJerychIaFsatr5zr/7/l8aNGhAgwYNeO655xgxYgTz5s3jnnvuoU2bNkRERJSb5lVdzGYz+/btKxtNOHHiBLm5uWWjROfz8/MjMDCQmJgYRo4cecnnatGiBXPnziU7O/uCozrjx4+nWbNmfP3112XTzS7Gx8eHo0ePljt2/honsAV5Xbt2pWvXrrz55puEhoayfPnycuvHKmNnZ4fFYil3rE2bNixZsgRfX98LZgfZvn07X3/9NXecSV2amJhIZmZm2fMNGzYkMTGRtLS0spG2vXv3XvJ5hLiackpz+GDPBwCMbTqWZ1o9w++fhVNaZKp8Xc7mDyB5H9i7UVDvDZJfehfFZMK5z+3U+vRTVNdJkHM+lZ0drv364dqvH6aUFDK/+ZbcX34hdepUVHo97vcMge6TbetAEnbAb+Ph4bXYdjW9thSjkdPPTUKxqnCuo8P9hXM3ik6lFzJjfRQAb97d9LJGXCrjpNcypkttHuoUyrqIVOZujWVffA5L9yexdH8S3et7M757GD3qe1c5sPIcPQpLQT6ZM78k7b33yPruO9vUNUVBQYGz97XOHCt7AJb8fLBacWjZklpfzkR73k2hqsgrMfHP8TTWHk1l88kMSk3nbgz6uujxcLTjRFoBH/11gsW7E/jfgIYMallxY8oLScwuZvT3e4jNLMLTyY4fxrWnRS33cmX8XO35YFgLJvy4n9lbYuhR34duAz/AO+kO8hMcMKWkkDHzS/xefumSrk3cWMw5OUQ89iQuFhN7Apsy+P2XqO19JrgtzoYVT9i+b/cwNBxQsQE7R+j0uO35wz/Dts9s04c3ToPtX0D7R6Dzk+cCpBvAJY+hPfDAA3z88ce8+eabtGrVivDwcNauXVv2wSshIYGUM1lqxIW5uLjwwgsv8NxzzzF//nyio6M5cOAAM2fOZP78+RetX1JSwqRJk9i0aRPx8fFs376dvXv3lgUbL730Ejt27GDSpEmEh4cTFRXF77//XunI26XS6XQ89dRT7N69m/379zN27Fg6dep0wU1j33rrLaZPn84XX3zByZMnOXLkCPPmzePTTz+96LlGjBiBv78/Q4YMYfv27cTExPDbb7+Vy/LXuHFjOnXqxEsvvcSIESMuOgoEcNttt7Fv3z4WLFhAVFQUU6ZMKRf47N69m/fee499+/aRkJDAsmXLyMjIqDSY+7fatWtz+PBhTpw4QWZmJiaTiZEjR+Lt7c3gwYPZunUrsbGxbNq0iaeffpqkpCTAto7qxx9/JDIykt27dzNy5Mhy19K3b1/q1q3LmDFjOHz4MNu3b+f1118Hzo28VeU8QlxN3x76lkJTIY08G/Fc2+fYszKOlOi8ytflxO+w3T0ECnwnkPT6+ygmEy79+1Prs8+uyyDn33QBAfi/NRWPhx4CIOW118hfvRrUGhg6G/RutkBu8wfXuKc2me+9hCGlCI2dlYAPZ6DS2oIvi1Xhf0sPYTRb6dnAh2Ftgi7S0qXTqFUMaBbA0ie6sHxiF+5sEYBaBVujMhnz/R76z9jCL3sTMZgtF28M8J44Ec8zN13NaWmY09MxZ2RgycjEknnmkZWFJTsbS04OltxcLLm5YLXiNngQIQvmVznIySw08NOeBMZ8v4d2767juSWH+OtYGqUmK8GeDjzavQ6/PdGFXa/czppnuvPZAy0JcLMnObeEZ34O556vd7A/Pvui5zmRWsC93+4gNrOIIHcHlj7euUKQc1a/pv5l6buf/zWcHO92qFsMxr9tHgDZP/5IaWRkla5P3HgUq5Wdjz6NS04GKU5eBLz/Pm1qn1lzqCiw8hnbmjWv+tBv2n83prWz7S82aZ9t6q1vU1vq9u0zbNPabiTKDSAvL08BlLy8vArPlZSUKBEREUpJSck16NmVsVqtyowZM5SGDRsqOp1O8fHxUfr3769s3rxZ2bhxowIoOTk5ZeUPHjyoAEpsbKxiMBiU4cOHK8HBwYqdnZ0SGBioTJo0qdzrsGfPHqVv376Ks7Oz4uTkpLRo0UKZNm1a2fOhoaHKZ599Vq5PgLJ8+fKyn2NjYxVAOXjwoKIoijJv3jzFzc1N+e2335SwsDBFr9crffr0UeLj48vqTJkyRWnZsmW5dhctWqS0atVKsbOzUzw8PJQePXooy5Ytq9LrFBcXpwwbNkxxdXVVHB0dlXbt2im7d+8uV+a7775TAGXPnj3ljlf2Op715ptvKn5+foqbm5vy3HPPKZMmTVJ69uypKIqiREREKP3791d8fHwUvV6vNGjQQJk5c2aV+puenl72ugPKxo0bFUVRlJSUFGX06NGKt7e3otfrlbCwMOXRRx8t+70+cOCA0q5dO8Xe3l6pX7++8uuvv1b4N4qMjFS6du2q2NnZKY0aNVJWrlypAMratWvLylzsPGPGjFEGDx5cpWu50d3I7w83g9jcWKXV/FZKsx+aKbtO71LijmYqXz62QfnysQ1K1L608oWLcxTl06aKMsVVyX9nmBLRtJkS0bCRkvTcZMVqMl2T/l8Jq8WinH79DSWiYSMloklTJe/vv21PHPlNUaa4KspUd0WJ235N+1i8d5cS0aiREtGwkZL33kPlnvtua4wS+tIqpckba5SknOKr1qeErCLlrT+OKU3eWKOEvrRKCX1pldL2nXXK38dSq9yGIT5eKT56VCk5dkwpiYiwPSIjlZLjx5WS4yeUkhMnlNKTJ22PqCjFmJxc5bb3xWUp93+7Q6nz8qqy/oW+tErp88km5eO/jitHk3MVq9Vaad1ig1mZueGk0vi8a5u4cL8Sn1l0wXO1mPqXEvrSKqXvp5uUlNyLv48VG8xK7483KqEvrVIenb9XsWbHKso7vkpin1AlomEjJea++xWr2Vzl6xU3jm3TPlciGjZSwhs3U35ZvK78kwcW2t533vJUlOQDl964xaIox1cryuzbFCV+V/V0+Ar9V2xwvkveMPRa+K9NgWRDQAHwzjvv8Ouvv3L48OFr3ZWravv27XTr1o1Tp05Ve2KPm4G8P1xbz258lg0JG+hRqwcftPmEJdP2UlpoonnPIHqM+Nf+Tb+NhyO/YtSEErtch7W4GNe77yZw+ntlC+NvNIrVSsorr5D3+x+g0xH85Uyce/a0bUoZvgjcguHxbdck5bS1pITY/j0xphfgWg+CfttjS5wAJGQV03/GFkpMFt4d0oyHOoVe9f7llZhYsjeBedvjSMkrxU6jZuH4jnSoU/kU5qthzZEUnlkSjtFsm5rWPMiNAc386d/Un3q+VV+snV5Qyqd/n2TJvkQUBew0asZ1rc3E3vVwc7CNqG08kc4TC/dTarLSJsSd78e2x92xaiOaR5PzuOfr7ZgsCtOHNmdE4QJMf31CzGp/rCbwn/ImHiMunNlN3HgSwyPIfvAB7Kxm9g97jIemPXvuyexY2+bFxkK4/U3bWpzLpSjXTdbIGtkwVIjrTWFhIUePHuXLL7/kqaeeutbdqXHLly9n3bp1xMXFsX79eiZMmEDXrl0lyBHXnf1p+9mQsAG1Ss2zrZ7j7++OUVpoW5fT5d5/rcs5/Asc+RVF0XD6cB2sxcU4tGtL4PvTb9ggB2yblwZMm4bLwAFgMpH01NMU7dwJAz8Ajzq2zF+rnitbK3I1pb/zGsb0ArQOFvynvlMW5CiKwsvLDlNistApzJMHO4RcpKWa4eagY0KPumz5X2/6N/XDaLHy6IJ9nEovvCb9mbc9lomLD2A0W+nT2I9tL/Vm5VPdeLJ3vUsKcgB8Xex5f1gL/nyqO93qeWO0WJm1JYbeH29iwc44ftufxKPz91FqstKroQ8Lx3escpAD0CzIjRf7224kvL0ygpiGj6Lz9cOnuW0KW/qnn2FKT7+kPovrl9lo4vizL2BnNXMipCn3v3XeZyGL2baHl7EQQrpA12ev7GTXSZBzKSTQEdfUe++9h7Ozc6WPgQMHXrT+pEmTaNu2Lb169eLhhx++Cj3mgv11dnZm69atNXrugoICnnzySRo1asTYsWNp3759hT2DhLjWrIqVj/fa9jIZVn8YOTvUpJzKQ2evod/4pmjPzzqVE2/bXwbINt1JyZGTqB0dCXz/fVRVyMxYbDQzcdF+Or23gXdWRXAyraBGrulyqbRagj78EOfbb0cxGkmc+CTFR07Y5r2rtXBsmW1D0auoaPsOcpatASDg3kZo2t1b9tzsLTHsiM7CXqfmg2EtUKuv7QcbnUbNjAda0zrEnbwSE2Pn7SGjwHDVzm+1Kry3OpK3VkagKPBQpxBmjWpLLY8Lb1RbVU0CXfnxkQ7MG9ueuj5OZBcZefP3Yzz/qy2d9+BWgcwZ3e6y9hka3y2MrvW8KDFZeHrZScy938SjXhH2XhasBQWkTpl60Q2wxY1h7ZRPqZUaS5HOnhYzPsROe9775tZPIGkP6F1h6CzbWsFbjExdE9dUdnY22dmVL8h0cHCokHr7enDq1KkLPhcUFFSlZAji6pD3h2tjTewa/rflfzhqHVnW/3dWvXMCs8FC30ea0KD9eXuuWczww52QuItS+9bELcpGMZkIePcd3O+998InOCOz0MAjP+zlUFJeueMtg925v10t7m4ZiKv9tc9sBmA1Gkl6chJFW7eidnQkZN73OOSts+1poXOCx7eCV82PzFry84kZcDvm7ELcGxgJWLjVts8MsDM6i5Fzd2FVuGZT1i4kq9DA0G92EJ9VTItabvw8odNlbzRaVQazhRd+PczKQ6cBeLF/Qyb2qlsjmz2aLFZ+3pPAZ+ujyC4yMrZLbd68q8kVBZqpeaUM+HwLucUmHutRm1eSn6L02CFi1/mBRSHwg/dxGzy4Gq9CXG2Htx2ACaPRWS0kPvoC/Z5/5NyTSfts+3cpFhg6B1rcf+06WgOqOnVNAh0hxE1L3h+uPoPFwKDlgzhddJpJrSbROqEve/+MwzvYmftfbV/+Q+Lmj2DjuygaF2L3tMQQFYNz797U+vqri36YjMssYsy8PcRnFePhqOP5fg3ZGpXBhsh0zFbbnzV7nZo7mgVwX7tgOtbxvOajE9bSUhIff4LiXbtQu7gQMu87HPa+DPHbbLuTj1tjS+9ag05Pfoa81X+jczYT9tlzqLtPBCAtv5Q7v9hGZqGBoa2D+OT+ljW+e/ulis0sYtg3O8guMnJ7I19mjWqLtoY2YMwrMfH4j7bNObVqFR8Ma8GwtrVq5FznKyg1kZhdQuMAl2p5/dceTeXxhftRqeD3QXa0+OteMo85k3HEFbWrK2ErV6Lzq8ZUwWfTdqtlwlBNKyoqYWv/IYRmJhBbvxUDfl+E+uzrbiiEWd1tqaGbDbONIF9n/5+v1C23RucGiNeEEFeZvC9cfT9F/sTpotP4Ovgyou5IDm+0pTVv0z+0/Ae3pH2waToAGUUDMUTFoPHwIOCdty/6Ae9QYi7DztzdD/Z04LcnuvBQp1BmjWrHrldv5/U7G1Pf15lSk5VlB5MZMWcXvT7exJf/RJGSV1Jj134xant7gr/+Coe2bbEWFJA4fgKlLV+2bcKXEg5Lx9lGuWpI/t9/k7f6b1ApBN7lj7rrY4BtNGHS4gNkFhpo5O/CtHuaX3dBDkAdbyfmjG6HXqtmw/F0pvxxrEb+j6fklXD/tzvZGZOFk52G78e2vypBDoCLvY4mga7V9voPaObPiA7BKApM2KjG0Gw4Xo0LsfdRYc3PJ/XNN6vvNTQWY509EPPH7SAnrnraFBe04qUPCM1MoMjOgY5ffnQuyAFY+7ItyHGtBXd+etMFOZfihg90NGfmcF/K7vFCiFtDcXExQLmNYEXNyS3NZfbh2QA81eYpondmYyg24+brQN025901NhTYsqwpFopd+5H1xw4A/N+aitbb+z/PsfF4OsNn7yKryEizIFd+e6ILYT7nFoN7O+sZ3z2Mv5/rwfKJXRjRIQRnvZaE7GI+/vskXd//hzHf72HJ3gR2nMokOqOQIkPNBRf/pnZ0JHjWt9i3aIElN5eEp17B0O0z0NrDybWw6pkaSU5gzswk9fVXAfBqXIzj49+Uzdf/YM1x9sbl4KLX8s1DbXGwu37n8bcN9eDz4a1RqWDR7gS+3RxTre2fSC1g6Nc7OJFWgI+LniWPdaZHg0vbQPR688ZdTQjzdiI1v5SXCx8E91oEtktDpVFRuHkzeSuqZ52nceHTnPomhlPziyj5ZBiU5FZLu6KiDau20XzDUtsPT72AV+h5gXjkSjj4I6CCe769Jlkdryc3/NQ1RVFISEjAZDIRGBhYPqIVQtySFEWhuLiY9PR03N3dCQgIuNZduiV8sOcDFkYupKFHQxb3/4lFb+6mOM9I71GNaNI18FzBFU9C+EKsjrWIWReAKTEZt8GDCfzg/f9s/5e9ibyy/AgWq0L3+t5881BbnPUXX6dRYrSw5mgKS/Ymsju28jWBLvZa/F3t8Xezx8/VnoAzX88eq+3tVKVzVZUlL4/4seMwREai9fUl9K1HsNvyDChWW/rX29+stnMpikLSE49TuGkLencTdaYMRzXwXQBWH0lh4qIDAHz7UFsGNPP/r6auG/O2x/LWyggAPh/eisGtrnw9566YLB5dsI+CUjN1fZz4YVwHgj1rdirh1XIkKY+h39hSTs/rWUTv3Y+SGeFMxmFX1C4uhK1aie7Mxu+Xw7z1B+KenYapyPZ/RGNvoc74+uieWAEaudFUnVIyCwi/+x5q5yST0rwjvX+Zd24EMD8FvukCJdnQ9Rno+/a17WwNumXW6IBtNCc2Nhar1XoNeieEuF65u7vj7+9/XU7DudnE58czZMUQzIqZ2X1n4xodwqZFJ3D20PPQO53RaM/chDq2An4dA6hIyR9O7urNaAMCCPvjdzQuLpW2rSgKX2w4xWfrTwIwtE0QHwxrge4y1mfEZRaxdH8SBxNzSM0rJTWvlCKj5aL19Fo1d7YIYGTHUNqEuFfL75Q5J4eE0aMxRJ1C6+tL0OP9cDz5oe3JgR9Cx8eu+BwAucuWk/Lqq6BWqDNMh/0bu8HOkVPphQz+chtFRguP9QzjlYGNq+V8V8s7qyL4blssdho1Cx7pQKcwr8tua+Wh0zz/yyGMFivtQj2YO6bdJaV0vhF8uzma99ccx0GnYXebdbgcnEvcxgBKM1Q49ehO8KxZl/V7bUk4SvzwoRiyNei8nVG7emKISUDvbqL2CwNQD5t5S0+dqk5Wq8LsR16h587fKbJ3psnaP3H09z37JCwaBtH/gH9zGP8PaG+u3+Hz3VKBDoDVapXpa0KIMjqdrmxqq6h5kzdNZl38OroFdeOr3l+xaMou8jNL6XZffVreHmwrlJdsu9tYmkuh5wgSv94MQMgP83Dq1KnSds0WK2/8fpSf9iQC8GTvurzQr2G1Bq8FpSbS8ktJzTOQkldi+z7fFgSl5peSkltKVtG5vy+N/F0Y2TGEIa2DcLnCrG7mjAzix43DeCoaNBp8B7fGU/eH7fru/R6aDb3sthVFoWDtWlJefRVrSSk+LfPxnjYf6vWhyGBmyFfbiUovpGMdTxaN71hjC/tritWq8OTiA6w5moqrvZbfnuhCfb/Kg+X/MndrDO/+GQlA/6Z+fD68Nfa6m++9w2pVeHDuLnbFZNOrrjPzDC9gjI4hdp0filkhYNo03Idd2u+btaiAxMHdKE4yonFUU3vpSlR6PbFDh2DJK8Q5qIRab7+AquukGrqqW8uiH/+ixXuT0SpWtFOnUX/4ef9eu2fDmhdt02Af2wI+DS/c0E3glgt0hBBCXBsH0w8yes1o1Co1S+9einLKhXXfRWDvpGP0e13Q6TW2u40LBkHcVsweLYn5VcGSmYnH6FH4v/pqpe0WG808tfggG46no1bBW4ObMeoapDxWFIXwxFwW7U5g5aHTGMy22QOOdhoGtQxkZMdQmtdyu+z2LYVFpE6dSv6qVQA4N/ElsOEhNI46eOg3qNPjkts0Z2aS+vY7FPz9t62vvgZCJvVCdf/3KIrCMz+H88eh0/i66Fn1dDd8XW7MrISlJgsj5+5mf3wOQe4OLJ/YBV/XC1+LoijEZxWzJzabXbFZ7InNJinHlqBiTOdQ3ry7KZprnJ2vJsVnFdHvsy0YzFbm9lHRZ/tDZEU4kH7IFbWzM2Er/0BXxam+isVC8sj+FIQno9YphMz5GodOtwFQfPAgCaNGoZgteDUuxHf6LGh0xxX33xATAxYL+vr1r7itG83R2AwSH3iA2vkp5LbvTqcF543AZUbBt93BXAIDP4KOE65tZ68CCXSEEELUOEVReGj1QxzOPMyw+sOY0nkKS97dQ1ZyER0H1aHdHXVsBbd/DuveRNE6knz6Lgr+2YZd3brU+W0p6kpSf2cVGnhk/j7CE3PRa9V8MaI1/Zte+/UjecUmlh1MYtHuBE6lF5Ydbx7kxsiOIQxqFXhZ+7soikLukl9Ie+89FKMRnbsdQe2TcQiwh3GrIaBFldvJX72atHfexZKbCyoF7yaFeLdRo3pmHzj7Mn9HHFP+OIZGreLnCZ1oX9vzkvt7PckpMjL0mx3EZhbRNNCVXx7rjNOZ9VSKonAqvZDdsdnsjs1mT2wWafnlNxzVaVS80K8hE3qE3RLTXGdtjmb6muO4OejY0Wk3jts/Ju4fP0ozNTh160bwnNkXfR0URSHt+QnkrN6GSq0QPOVxnB54tlyZvD/+4PT/XgIgoEsx7tP+gICWl9VnRVHInjeP9I8/AasVfb16uA4ehNtdd1U5MLuRFRvNfDv6RQaGr6XY0ZUW61aj8zozVdNihu/7QfJ+COsFDy2/JdJ7S6AjhBCixq2NW8uLm1/EQevAn/f8SdEpFX9+fRidXsPo97pg76SD0+Ewtw9YTeR5Pc7pr/4ArZbaP/+MQ7OmFdpMyCpmzLw9xGYW4e6o47sx7Wgben19GFcUhb1xOSzaHc+aI6kYLbZRHhe9liGtgxjdOfSyplGVRkSQ9OxzmBISQA1+rfLwaO2Mavzf4FH7P+uaMzJIffttCtatB0DvbiKwYw72jRrD3Z9Drbbsj89h+OydmCwKr9/ZmPHdwy65j9ej+Kwihn69g6wiIz0b+NCroQ+7Y7LZE5dNdlH5ae12GjUtg93oUMeTjnW8aBPqUa2JJq53ZouVwV9t59jpfAY19+GLov9hOH6E2L/8UCxKlTbszfjsQzJnzQMUgka3xfXVRZWWS//0U7Jmz0GlVgi5U4XjGxvKNqitKkthESn/e46Cf7baDqgUUM4EYioVju3a4jZ4MC79+qG5ST8jfvzFcgZ88zoaxYr7h58QMOi80bEtH8E/74LeDSbuALerkwr9WpNARwghRI0yWowMWjGI5MJkJraayOMtHmfZR/tJjcmndb8QugytZyv4XX9I3IUpsD8xc+Ox5ufj/dQkfJ58skKbhQYzd32xlbisYoLcHZj/cAfq+TpXKHc9yS4ysnR/Iot3JxCXZUtprlbB6M61eb5fg0tex2MpKCDl9Tco+OsvAFxqlRDQ3wPNxHXgVDH9tqIo5K/6k7R33saSX2AbxWlagHcbO1T93oA2Y0CtIavQwJ1fbCM1v5Q7mvvz1YNtbqoRjPDEXIbP3kmpqXxiIr1WTZsQDzqG2QKb1iHuN+UanEtxNDmPwV9tx2JVWDzEnS7rh5J1VEN6uJttCtsfv6MLrDwgyfnpJ1LfsmXz8uvliOfM7aCrfLqgYrWS/NQkCjZsRKO3UPtBb+yeWwd2TlXqpyHqJEkTxmBMyQWVgl/bItyaOJEfmUd+nAPFGfqysiqdFufevXEbNAinHj1Q290cC/H/OpgAE0YRUpCOscfttJz95bknUw7BnNvAaoZ7ZkHL4deuo1eZBDpCCCFq1Pxj8/l438f4OPiw6p5V5MQaWPHpQTRaNaOmdcbJTW8bzZndE0WlIzF6AEV79mPfogW1Fy9Cpa14F/35Xw7x24EkAt3sWfFk1/9cb3G9sVoVdsZkMW97HOsj0wDwddHz5t1NuLN5wCUFFYqikLNwEWkffABmMzonM0FD/HD431rQnwv8TOnppE55g8KNWwDQexgJ7FiAff+HoddLts1IAYtVYfT3u9l+KoswHyf+mNTtphzF+Od4Gm+tjCDUy4mOdTzpWMeTFrXcsdPe/FN5LtX0NZHM2hxDgJs9G7tFol/3GvEbfSnJ0OLUpQvB382t8Dub//ffJD/zLCgKXs1L8f3mb/D+7/Uy1uJi4kc8QOmJU+jdTIQ+0RbN6MVl+zhdSP7CmaR88DVWE2gdLAQNC8Hxsa/Bqx4k7YFjyzHtWkFeZCF5cQ4Y88/dUFA7O+J6x524DR6MQ+vWqG7QqVxp+aX8MOYFBkVuoNTFneZ/r0brYfs/jakU5vSG9AhofDfc/+Mtld1OAh0hhBA1Js+Qx8BlAykwFvB2l7e5p/49/PFFOIkR2TTrEUTPB89k/FkxEcIXkV3YjbRVMajs7amzbBn6sDoV2vw9PJlnfg5HrYKfJ3SmQ53ra7rapdhyMoM3fj9K/JkRnu71vXlncDNqe1ftTvZZJYcPk/z0U5hS01GpFfz6BeD+0VrQ2pH/+x+kvjMVa1EpqBV8mhbgdWd7VHe8D76NyrXz0V/H+WpjNI52Gn5/sutlTasTN5cSo4UBn28hPquYMZ2CeSvvNQyHthP7tx+KGfzffguP++8vK1+0Zw+JjzyCYjLjHlaE/7T3UbV+sErnMqWmEjf0HszZuTgHllLrfw+iGjCt0rJKfjrpL44ie3MCAI7+VoKmvoC25/iKH+StFkjYhXJ0GYZtf5AXWUp+vAPm0nNBlEPzRgTP+xGN8/U9MvxvVqvCK+8uZtTiaWhQ8P9iJh79+pwr8PcbsOMLcPKBibsqHe29mVU1NrgxQ1whhBDX1KzDsygwFlDfoz6D6g4iPT6fxIhsVGoVrfuF2AoVZcKRpRjyNaSvSwLA94UXKg1yErKKeW35UQCeuq3+DR3kAPRo4MNfz/bgmdvrY6dRszUqk34ztjBj/UlKTRfft+cshxYtqPP7Hzh3boNiVZG6NpXTI/uSNO5BTr/8MtaiUuw9jNS5T4/3u3NRjVleIchZH5HGVxujAZg+tLkEOQIABzsN0+9pDsCC3Ykcbvseeh8nfJrlAZD+wYeYkpMBKD1+nKSJE1FMZpyDSvB/eGCVgxwAnb8/tWbNRqXTUnjanvRvFsD+H8oXUhTMm+eSMLh7WZDj2SOEkD+2ou31aOWjFWoN1O6K6q5PsH/vOH4zFlHv7TsI6W/CrXYxKq2VkiPHSXp0HNbS0kt/ka6hRVtP0uePWWhQUPe/o3yQE78Ddsy0fX/357dckHMpJNARQghxSRLzE/np+E8APN/2eTRqDQfWxgNQv70vrt4OtoL7fwCLgbRjwSgGI05dOuPx4IgK7ZksVp7++SCFBjPtQj146rZ6V+tSapS9TsNzfRvw13M96F7fG6PZyoz1UQyYsYWtURlVbkfj5kat7xfi+/AQUCnkH86gcFe4bRSndSm1p0/E/o090HBghQ+Da4+m8twv4QCM7VKbwa2Cqu8CxQ2vSz1v7m9XC0WByX9nY+r/Pp4NinDwMWEtKiLljTcwJiaSMP5RrIVFOPgYCLrLE9Xdn1RoKzWvlIfm7ubumdtIzasYVDg0b07gh7YNcbNPOJPz+WsQvdH2ZGYUJdP7EvvshxSnqFHpIOi1J/Cb/RcqV9+qXYxaA3V6oBo0A6dPjxP41Q+EDvdFrbNSfPAoyc88g3KD7LeYkltC6bS3qVWYgdHdk3pvv3nuSUMBLH8cUKDVQ9DozmvWzxuBBDpCCCGqzGQ1MXXnVMxWM10Cu9A1qCs5qUVEh9s+uLfpf2afG4sZ9n2PIV9DUbwRVCr8p06tdK78jPUnCU/MxcVey4zhrW64jSsvpo63Ewse7sDMEa3xddETl1XMqO/2MGnxAdLyq3aXWaVS4fW/6YROHYedixkHbwN1nu6I9+xdqHo9D1p9ufJ5JSYmLwnn8YX7KSg106G2J6/e0bgGrk7c6F69ozHeznacSi/ky8x2qJrcRWCHbFRaKNqxk9ihw7BkZqJ3MxHcqxD18B/KrRMD2BeXzV0zt7HtVCZHkvMY9d1ucooqBhWuAwfiPcm2eWjqXheKvhiHsuYVcp67jbiFSZhLNNj5u1Fn6XJcRz19+Rel0ULd3jg89RPBfQyoNAqFm7dw+uWXUSxVH1G9Vlb/bxrdEg5gUWsI+/RjNG7n7dP19+uQGw9uwTBg+rXr5A3i5vprIoQQosYoisL03dPZk7oHB60DL7W37ZFx4K94UKBOS2+8As98ADq+CvKTyYmzTalw7tULu5CQCm3uiM7k6022aVXvD21BLQ/Hq3MxV5lKpeLuloFseL4nY7vURq2CVYdTuP2TzczbHovZYr14I4DjAy9Rd9kiav+2EvvH54OLX4UyW6MyGDBjC8sOJqNWwRO96vLj+A6yIF9Uyt3RjqmDbGnev94cTXSnadj5e+Lb3DaFzVpQgM7JQnCvLDR3vVthT6fFuxMYMWcXmYUGGvq54O9qT1R6IWN/2EuhwVzhfN5PTsR1YH9QVCRv1HP6s8Wk7nUGqwqX3l2pvWo9+oaNKtSLzSzi9k820fX9f/hg7XGi0goufnFutXAcP4NaXbNBrZC/eg2pb73N9bw8fevsn+iy5TcANM+9iEuXzueejFp3bsrfkK/BXtatX4y86wkhhKiSRZGL+PXkr6hQ8WGPDwlzD6Mgu5STu20ZxtoMCD1XeM9srCYVeTG2rGkeI0dWaC+7yMhzS8JRFHigXTB3trj5N/5zsdcxdVBT/pjUjZbB7hQazLy1MoKBn29l8e4ESoxVuNsc3AH8Ku4/VGw088aKo4z6bg8peaXU9nLk18c789KARui1t3Y6ZfHf7mweQJ/GvpgsCi+sTsZy1+d4NCjCNaQEO3cI7pmJrvUd0OHRsjpGs5VXlx/h1eVHMFkU7mjuz7KJXfjxkQ64O+o4lJjLYz/uw2Au/zutUqkImP4+9k0bYzGqyU9wBLUK3xeeJ+jrOZUmDTianMd93+4gOqOI5NwSvtkUTd/PtnD3zG3M2x5LZqGhQp0yTYfgfOf9BHXKARXk/vIL6R9/fF0GO1n7DuL6uW2UJqbn3TR+dMy5J4uz4XfbaBgdn4A6Pa5BD288knVNCCHERW1N2sqkfyZhVay80O4FxjS1/QHesuQkRzYmUauRB4OfbW0rnHoUvu1KTrQzqXtdsQsNJWzN6nLT1hRF4dEF+1kfmUaYjxOrnuqGo93Nl+74v1isCj/tSeDDtcfJL7Xd+XZz0DG8fTAPdQol2LPqo1v747N5/pdDZfv4jO4cyssDG91yr6m4fCl5JfT9dIst+B7UlDEZH8HBhSgKqNxqweNbwdGWJCS9oJSJCw+wLz4HlQpe6NeQib3qlqWjDk/MZeScXRQZLQxo6s+XD7auMCXVlJ5OwqjRWIqKCPr4Y5w6day0XzuiM5mwYD+FBjPNglx5tHsYKw+lsOlEOmar7SOsVq2iZwMfhrapxe2NfSvuk2QohNk9yd1zmpS97gD4PPss3o8/Vo2v4JUxpaZy5O6hOBTkcKRWU+5etRgH+/P2Alr6MBz9DbwbwGNbQOdw7Tp7HZD00kIIIapFVE4Uo9aMoshUxND6Q5naeSoqlYrifCMLXtuBxWRl0LOtCG50JlPaH0+h7F9A7Ma6GNJL8Hv1FTxHjy7X5o8743jj92PYadQsf7ILTQPdKjnzrSGvxMSv+xKZvzOOxOwSwLbhaN8mfoztUodOYZ4X3IPHYLYwY30UszZHY1XA39Wej+5rQff6PlfzEsRN4uz/Syc7DeuebEPgz/0gLwnGroKQToAtiHn8x/2k5pfiYq/li+Gt6d2oYsKA7acyGTdvL0aLlQfaBfP+sOYVfo8VoxHU6kr31AJYezSFp38Kx2ix0jnMi9mj25ZtwJtVaGDV4RSWHUjiUFJeWR0Xey13tQhgaJtatAv1OHfO0wdhbl+yIuxID7e93/i9/jqeD1Ucbb7arMXFRN4/AvWpk8S6+uM25we6tjxvhPzob7ZAR6WB8esgqO216+x1QgIdIYQQVyyrJIuRq0eSXJhMO792zO47G53G9kFj14po9q+Nx7e2K/e+1Nb2gaI4Gz5tQlGyhYSN3qgcHam/aSOa8967j6fmM+jL7RjNVt64qwmPdKuYbvpWZLEqbDyezg874th2KrPseCN/l7KMaQ525+5UR5zOZ/Iv4RxPta1VGNo6iCmDmuLmoKvQthBVYbUq3DdrJ/vjc+jd0IfvH6iPylgI7sEA/LovkddWHMVotlLP15nZo9oS5nPh/WnWHk1l4qL9WBWY0COMVwY2qvLGuYt3J/D6iiNYFRjYzJ/PHmhVcaTmjFPphSw/mMTyA8mcPi/jW7CnA2M612Zc1zpo1CrY/jmse5OMCE8yD9um1Qa8Px33IUOq+ApVP8VqJfGppynasIFcOydWT5zOO4/3PVcgPwW+6QwlOdDzJej96jXr6/VEAh0hhBBXxGAxMP6v8YRnhBPiEsKiOxbhbu9ue67EzIJXtmMstTDw8eaEtTozgnDmg0TS/toURBlxf+ABAt6aWtZmqcnCoC+3cTKtkF4NfZg3tn2VP/jcSqLSCvhhRxzLDiRTcmbfHXdHHQ+0D2Zkh1BWHj7NjPUnMVkUvJzsmHZPMwY0u/nXOImaF5VWwJ1fbMNosfLFiNYMahmIyWJl2p+R/LAjDrCNNn56f8uy0ZX/8sveRP7322EA/jegIRN7/Xf6eEVR+GrjKT7++yQAD3YM4Z3BzWyBykVYrQq7YrNYfiCZ1UdSKDqz5q11iDsf39eSul6O8OMQlJjNpJ0IIye8FDQaan0+A5c+fS7Ses1I/+RTsubMwaTW8O7tk/hm+ji8nM9kUVQUWHQfnFoHAS1h/AbQyI0MkEBHCCHEFVAUhVe3vcqqmFW42Lmw8I6FhLmFlT2/f20cu1bE4BHgxIg3OqBSq2y7lH/eClNKEqdWBYBVoc7vv2PfsEFZvddXHGHhrgS8nfWsfbY73s76yk4vzsgrNvHLmWltSTklFZ7v18SP94Y2l9dRVKvP10fx2fqTeDnZseSxzry+4gi7YrIBeLZPfZ6+rT7qKgQeZ83ZEsO01ZEAvHdPcx7sWDEDI9gClbdXRZQFVE/dVo/JfRtc1s2QEqOFZQeTeH/1cQoMZvRaNf8b0IhxzfWov+2KUpxNSlxn8nbHo9LpCJ71LU5dulzyea5E7vIVpLzyCgAfth3BHc+N4752wecK7P8BVj4DGr1tXY5vxWx0tyoJdIQQQly2OYfn8MXBL9CoNHzT5xs6B55LcWoyWvjxtR2UFJjoM64JDTv6256IXAVLRpIR6UvmIS2O7dsT+uOCsnp/HUvlsR/3A7Dg4Q70aCDrSKrKYlXYEJnG/J1xbD+VhYtey9RBTRnaJkhGxES1M5qt3DVzKyfTCtGoVVisCk52Gj57oBX9mvpfVpsfrj3O15uiUalg5ojW3NUisMI5X/j1EH8cOg3AlLubMK7rlU9rPZ1bwku/HWZrlG06aIc6nnzVJhWfP8eiWCE5+Q4KtoejcnQk5Lu5OLZuXa6+YrViTk/HGJ+AKTEBY3wCxgTbw3T6NPqwMNzvHYbrwIGoHaueQKR4/37ix44Fk5mfG9zOybseYvGjHc/9f07cAwuGgKkI+k2DLpOu+LW4mUigI4QQ4rKsi1/H5E2TAXij0xvc3/D+cs8f2pDItl+jcPGy56G3O6E+m01p/t1YT23h1No6WAoMBM34DNcBAwBbRqeBn28lt9jEhB5hsnnlFUjIKsbZXounk93FCwtxmfbH53DvtztQFNumt7NHtaW+n8tlt6coCq+tOMri3QnoNCrmjmlPzzM3O4qNZh5feIAtJzPQqlV8cn9LBrcKqq5LQVEUFu9JYNqfkRQbLTjaaVge+hsNE3/B6uBHUmQninbtRe3qivcTT2BOS7MFMokJGBMSUQz/kb76DLWTE6533YX7fffh0Kxi+vfzGRMTibv/ASw5OWwLbM7Hncew5tmetvVOFjNs+cj2UCwQ2g3GrIRKNlu+lUmgI4QQ4pIdyzrG2DVjKbWUMrLxSF7u8HK557OSC1n6/j7MJis9H2xIsx5nPoykR8LXnciLd+T0Tne0vr7U27AelU6Hxaowcu4udsVk0zzIjd+e6CKbVwpxA1i6P4kTqflMuq1+tSS5sFgVnvn5IKsOp+Cg07BwfEfCvJ0Y98NewhNzcdBp+OahNvRqWDGLW3VIyCrmxaWH2B2bjT0G1jtPpZY5HmvtfiT8paPk4MHKK2o06GoFYRccgi4khCw3XyJUroSX6miffpKm4ZuwJCWWFdc3aYzHfffhetddaFzKB4eWggLiRozAeCqaWM9aPNflCZ4c2Jynb68P2TGwbAIk7bUVbn4f3PkJ2N+6WSkvRAIdIYQQlyStKI0H/3yQ9JJ0ugZ15cvbvkSrPpf21VBi5tf39pKXUUJwE0/umtTy3Dz9Vc/Bvu+J216fksQivJ9+Cp+JEwH4dnM07685jqOdhj+f7k4db6drcXlCiOuA0Wxl/IJ9bDmZgau9Fm8XPTEZRbg76pg3tj2tQzxq9PxWq8IPO+L48K/j1DbH8Yfd69ipzJh7vkvayjisJSXYhQSjCwnBLiQUu9AQUu3d2B6Xx7aoTHZEZ5JTbCrXpru9hlcCi+kcuY3i9etRTLbnVfb2uA4YgPv99+HQujVYLCQ+MZGirVspdvFgQpdJeIYG8edT3bA7+jOs+R8YC0HvCnd+Ci3uq9HX4kYmgY4QQogqKzYVM3btWCKzI6nrVpcf7/gRF7tzdyIVq8KaWUeIPZSJs6eeB17tgL3zmTu8JbnwaWNK0kzE/e0DOh31/9mA1seHUpOFTtM3kFts4oNhzXmgfeWLkIUQt45io5mH5u7mQEIuAAFu9vz4SAfq+V7+1LhLFZNRyPO/HqJV8k9M0f2IUWVHwai/8QprTW6xkR3RWWw7lcn2U5nEn9mI9yxnvZZOYZ60DvFgxcFkotILy67jhU7+9IzfR97SXzGeii6rY1evLnZBtSjcvBlFr+fpzk9wyr0Wy8Y2os3htyDid1vB0K5wz7fgLu+V/6WqsYFsmSyEELc4i9XC69tfJzI7Eg+9B1/e/mW5IAfgwN/xxB7KRK1VMWBC83NBDkD4IjAVk5NUGzDi2q8fWh/b3PsVB5PJLTZRy8OBe9sGI4QQjnZa5o3twGML92E0W5n5YBuC3B2uah/CfJxZ+ngX5mzxY/M/R+ipDid7wSge8/qc/adLOX8YQKNW0TrYnW71velWz5uWwe7ozqxNfLxnXZYdSOLTdSdJySvl+b/iaOBXmxc/mEsXw2nyfl1K/po1GE9FlwU+c7qP4ZRzLV5vnEabP5+HghRQa6H3a9D1GVBXvl+QuHQyoiOEEDeZIlMRa2LXkGvIpdBYSJGpqMKj0FRIsanY9tVsu1upU+v4rv93tPYtn3Uo8Xg2Kz8PR1Gg18iGNO1+3iJhqwVmtsGSGk/UqmAUk5nQxYtxbNMaRVEYMGMrJ9IKeO2OxjzaIwwhhLjeRMfG4LmgNx5KLsetwaQr7uj09rg5O+Hh6oK3mzM6O3vQ2IHWzpbu+ez3bsHg24RStzAW7Enmq43R5JXYpq61C/Xg5YGNaO2lI//PP8n/6y/2hrbm1dIQ3nRYykPKSlsHvOrDsDkQ2Po/einOJyM6Qghxi/p036f8cvKXS6rjoHVgauepFYKcguxS1n13DEWBRl0CaNKtfEpYotZBThy5iV4oJjP6Jo1xaN0KgJ3RWZxIK8DRTsP97WU0RwhxfapbJwzz8NkoPw2nkTqRRiSCGcg986gCe7WOCd4NGNu4ETsK/Fkc58yR+Frc+202fRr7878Bd6K+7S6+/3wxv9u9QWMlwVax3cPQ712wk7WLNUECHSGEuInkGfL4I/oPAAbUHoC3gzdOOqdyD2edM446R5x1zmXHXOxcsNOUT1dsMVn5a85RSgpMeAc703N4JRv37ZmFYoWcWA+gGM8HHywr8/32OACGtalVLRmbhBCipmgb9ocnd0PaUbAYwWywfS373gQWQ/nvTaW2TGnpkWAsgPRj2KUfoxfQSwNoIF9x5Hh0MHu+DMaic2G5dhV6lQnF0RvV4C+h4cBrfOU3Nwl0hBDiJrIsahmlllIaejTkwx4fXtFmktuWRpEWm4/eUcuACc3R2v1r3nhmFET/Q2GqPaasYtRubrjeeSdgS+O64XgaAGO61L7sPgghxFXj08D2uFSKArkJkB4BacfOfI2ArChcrcV0UJ2gAyfACqigJPQ2HO79Flz8qv0SRHkS6AghxE3CbDXz0/GfABjZeOQVBTkndqdydHMyAH3GNcHNp5KFwntmA5CTHAoU4D5sGGoHW7n5O+NQFOjRwId6vs6X3Q8hhLjuqVTgEWp7nD9CYzZC5klIjyAtaj+pccfRNbidJnc9ZasjapwEOkIIcZPYnLiZlKIU3PXuDKxz+dMhMpMK2bTwOADt7qxN7ebeFQuV5kP4YowFGoqiC0ClwmPEcAAKDWZ+2WvbPG9c19qX3Q8hhLihae3Avxn4N8Ovxf3I+M3VJ1tTCyHETWJh5EIA7m1wL/Za+8tqw1BsYs2sI5hNVkKaeNL+zjqVFzz0ExgLyUmqBYBzjx7YBdsSDvy2P4kCg5kwbyd61ve5rH4IIYQQV0oCHSGEuAmcyD7BvrR9aFQaHmj4wGW1oVgV1v8QSX5GCS6e9vR9uClqdSXTK6xW2DMbq1lFbpTtz4jHQyPPPKUwf0ccAGO71q68vhBCCHEVSKAjhBA3gcXHFwPQJ7QP/k7+l9XGgb/jiTuciUarZsBjzcpvCnq+mH8g6xR5SR5Yiw3oQkNw6toVgM1RGcRkFuGi1zKsTa3L6ocQQghRHSTQEUKIG1xOaQ5/xvwJ2JIQXI7EyGx2/x4DQI/hDfANvcAGbFYLbHofRYGcBF8APEaMQKW2/TmZdyal9P3tg3HSyzJQIYQQ1478FRJCiBvcb1G/YbAYaOzZmFY+rapUx2KxUpxnpDDHQEF2CVuXRKEo0LiyTUHPt2cOJO2lJNcdw+l8VA4OuN9zDwCn0gvZcjIDlQrGdK595RcmhBBCXAEJdIQQ4gZmtpr5+fjPQPmU0oU5peRnlVKYU0phjoGiHAOFuYYz35dSnG9EUcq35RPiQo/h/7GHRE48bHgLgOzc1sAJ3O66C42bG0DZ2pw+jf0I8XKs1usUQgghLpUEOkIIcQP7J+Ef0orT8LT3ZECdAQAc3ZzE5p9OXrSuWqPC2UOPk7seD38n2t9Zp+KmoGcpCqx8BkzFmDw7UrA0GgCPkQ8CkFdi4rcDSYCklBZCCHF9kEBHCCFuYIsiFwG2lNJ6jR7FqnBwXQIAzh56XL0dcPbQnwlo7Mu+d/awx8FZh6qqWdHCF0HMRtDak1PQCcy/4tCuLfaNGgHwy95Eio0WGvq50DnMq0auVQghhLgUEugIIcQNKjIrkgPpB9CqtNzf4H7AllQgP7MUOwctD77VCd2FRmguRUEq/PUqACWhj5D1wXIAPEePBsBiVZi/Mw6wjeaoZMdvIYQQ1wHJuiaEEDeos6M5fUP74udk23P72NbTADTs5F89QQ7A6hegNA+LZwuS5+8BsxnXO+7ApW9fANZHppGUU4KHo44hrYOq55xCCCHEFZJARwghbkBZJVmsjl0NwMgmtpTSRbkGYg9nAtC0+39kTrsUEb9D5EoUlZbU4w0wJSWjq1UL/7emlo3czNseC8CIDiHY66opuBJCCCGukAQ6QghxA/ot6jdMVhPNvJrRwrsFABHbT6NYFQLqueEV6HzlJynOhj9fACBPO4j8f3aAVkvQJx+jcXEBIDIln10x2WjUKkZ1Dr3ycwohhBDVRAIdIYS4wZisJpYcXwLAg40fRKVSYbUqRGyzTVtr2r2apo/99RoUpWPQ1Cd1aTgAPk8/jUPLlmVFzo7mDGjmT4CbQ/WcVwghhKgGEugIIcQNZkP8BtJL0vGy96J/7f4AJBzNojDHgN5JS902Pld+klPr4dBirBYVybu8UEpKcezcCa/xj5QVyS4ysiLcFlw9LCmlhRBCXGck0BFCiBvM2SQE9ze8HzuNHQDHtiYD0KhzANorXSdjKICVzwKQntYVQ3QCGg8PAt//AJX63J+Nn/YkYDRbaVHLjTYhHld2TiGEEKKaSaAjhBA3kGOZxwjPCEer1nJfg/sAKMguJf5oFgBNu1VDEoIN70BeIgV5tcjZEgNA4PvT0fn5lhUxWaz8uDMekJTSQgghrk8S6AghxA1k8fHFAPSv3R8fR9sUtYhtp1EUCGrojoe/05WdIGEX7JmNqURNynZHADzHjMa5Z89yxdYcTSU1vxRvZz13NA+4snMKIYQQNUACHSGEuEFklmSyJnYNAA81fggAi8VKxPZqSkJgKoXfJ6FYFU4faYQlvxB9k8b4PP98haI/nElC8FCnEPRaSSkthBDi+iOBjhBC3CB+PfkrJquJFj4taObdDID4w1kU5xlxcNER1uoKkxBs+RCyosiK8ac4JheVoyNBn3yC2s6uXLFDibkcSMjFTqNmZEdJKS2EEOL6JIGOEELcAEwWE7+c+AWAkY1Glh0/m4SgcZcANNoreEtPOQzbZlCcqSPjgBYA/9dfR1+nTrliZouVD9YeB+CulgH4uOgv/5xCCCFEDZJARwghbgB/x/9NZkkmPg4+9A3tC0BeRgkJEdkANLmSJAQWM/z+JBaDldP7gsBqxfXOO3G7Z0iFou/+GcmO6CwcdBom9qp3+ecUQgghapj2WndACCHExS2OtCUhuL/h/eg0OoCyDUKDm3ji5uP43w2YSiA3EfISIPfsI9H2NScOpTCd1AO+mHKN6GrVwn/qlAqZ1H7ak8APO+IA+OyBVtTzda7eixRCCCGqkQQ6QghxnTuScYTDmYfRqXXc2+BeACxmK5E7ziYh+NdoTmEG7P4WsqPPBTRF6f95jrw4V/LjtKDVEvTJx2hcXMo9vysmizdWHAXghX4NGNDMv5quTgghhKgZEugIIcR17ruj3wEwsM5AvB28AYgJz6CkwISjmx21W3iXr7BxGuyfV7EhO2dwDwX3YHAPAfcQLHa+FEXlkLpiFlCKzzNP49CyZblqidnFPLFwP2arwt0tA3myt0xZE0IIcf2TQEcIIa5jJ7JPsCFhAypUPNzs4bLjx7baRnOadA1EozlvuaXVCidW277vPAlCOtmCGrdgcPBAsVopPXqUoh07KFy8nZLwQ2A2A+DUpTNejzxS7vyFBjPj5+8jp9hEi1pufHRvC9kcVAghxA1BAh0hhLiOfXvoWwAG1B5AXfe6AOSmFZN8IgdU0LjrvzbrPH0ACtPAzgVufxO0eoxJSRStXE/R9u0U7d6NNS+vXBVdSAjOPXrg/eREVOpzQZPFqvDszwc5kVaAr4ue2aPaYa+TPXOEEELcGCTQEUKI69SJ7BOsT1iPChWPtXys7PjZlNKhzbxw9XIoX+n4nyhWKDS3pei9Dyjcvh1TfEK5ImpXV5w6dsSpa1ecunbBLji40vN//PcJ1kemY6dVM3t0O/zd7Kv3AoUQQogaJIGOEEJcp2YdngVA/9r9y0ZzzCYLx3emAtC0e1DFSifWkBnhTObRk8BJ2zGtFoeWLXHq2gXnLl2wb9YMlfa/3/6XH0zim03RAHx0bwtaBbtXyzUJIYQQV4sEOkIIcR06kX2CdfHrbKM5Lc6N5kQfyKC0yISzh57QZl7lK2XHoKRHkhfnC4DrnXfieucdOHbogMa56qmgDybk8NJvRwCY2Ksug1tVElAJIYQQ1zkJdIQQ4jp0djSnX+1+1PM4l+Xs7LS1Jt0CUav/lRTgxBpKc3SYCrWoHBwIeOdt1I4X2V/nX1LySpjw436MZit9GvvxQr+GV3YhQgghxDWivngRIYQQV9PJnJOVjuZknS4k5VQeKrWKxl0CK1Y8vpqCBNs6GudePS85yCkxWpiwYD8ZBQYa+rkwY3irisGUEEIIcYO4rEDnq6++onbt2tjb29OxY0f27NlzwbLLli2jXbt2uLu74+TkRKtWrfjxxx8vu8NCCHGzO5tprW9oX+p71C87HnEmpXTt5l44e+jLVyrORonfSX6iLTmB64CBl3RORVF4YekhjiTn4elkx9wx7XDWy6C/EEKIG9clBzpLlixh8uTJTJkyhQMHDtCyZUv69+9Penrlu257enry2muvsXPnTg4fPsy4ceMYN24cf/311xV3XgghbjZnR3MAHm/5eNlxk9HCid1nkhD0qGTNTNQ6SrPUmIq0qBwdce7Z45LOO/OfU/x5OAWtWsU3I9sQ7Hlpo0FCCCHE9eaSA51PP/2URx99lHHjxtGkSRO+/fZbHB0d+f777yst36tXL+655x4aN25M3bp1eeaZZ2jRogXbtm274s4LIcTNZtahM2tzQvuVG805tS8dQ7EZFy97Qhp7Vqx44k/yE2yjOS69e6O2/+9U0IqikJhdzG/7k3jx10N8us6Woe3dIc3oGOb1n3WFEEKIG8ElzUswGo3s37+fV155peyYWq2mT58+7Ny586L1FUXhn3/+4cSJE3zwwQcXLGcwGDAYDGU/5+fnX0o3hRDihhSVE8Xf8X8DlNs3B84lIWjaPRDVv9fNmA0oURvIT3ACwPWOitPWFEUhOqOQ3bHZ7I3NZk9sNqfzSsuVGde1NsM7hFTX5QghhBDX1CUFOpmZmVgsFvz8/Mod9/Pz4/jx4xesl5eXR1BQEAaDAY1Gw9dff03fvn0vWH769Om89dZbl9I1IYS44Z3NtNY3tC8NPBqUHc9MKiAtNh+1WkWjzgEVK8ZupSTFgLnEFbWzM07dumGxKkSm5LPnTFCzNy6brCJjuWpatYrmtdzoUMeTLnW96VHfu0avTwghhLiarspKUxcXF8LDwyksLGTDhg1MnjyZsLAwevXqVWn5V155hcmTJ5f9nJ+fT/AFdu4WQoibQVROFH/H2UZzzl+bA3BwXQIAdVp54+Smr1CXE6vPTVu7/TY2x+XxzE8HyS81lyum16ppHeJOhzpedKzjSesQdxztJOGAEEKIm9Ml/YXz9vZGo9GQlpZW7nhaWhr+/v4XrKdWq6lXz7YPRKtWrYiMjGT69OkXDHT0ej16fSV/zIUQ4iY16/AsFJQKozlZyYWc3GN7z23TP7RiRUVBOb6GgrOBzoABzNwQRX6pGWe9lna1PehQx5OOdTxpFuSGXqu5KtcjhBBCXGuXFOjY2dnRtm1bNmzYwJAhQwCwWq1s2LCBSZMmVbkdq9Vabg2OEELcyk7lnCobzTl/3xyA3X/EgAJ12/jgG+pasXJKOCUxmZhLvVG7OJPaoBUH/tqBRq3in+d74uv630kJhBBCiJvVJc9ZmDx5MmPGjKFdu3Z06NCBGTNmUFRUxLhx4wAYPXo0QUFBTJ8+HbCtt2nXrh1169bFYDCwevVqfvzxR7755pvqvRIhhLhBnR3N6RPSh4aeDcuOp8bmEXsoE5UKOtwdVnnl46vL9s5x6dOXBYdtoz+9GvhIkCOEEOKWdsmBzgMPPEBGRgZvvvkmqamptGrVirVr15YlKEhISECtPpe1uqioiIkTJ5KUlISDgwONGjVi4cKFPPDAA9V3FUIIcYOKzo3mrzjbvmL/Xpuz+/cYABp28sczwKnS+krkavITbQGNU//+LNueBMB97WrVVJeFEEKIG8JlrUKdNGnSBaeqbdq0qdzP7777Lu++++7lnEYIIW56sw5VPpqTeDybpOM5qDUq2t9Zp/LKOfEUHz2JpdQbjZsr+73rkV4QjqeTHbc18qu8jhBCCHGLuOQNQ4UQQlSP6Nxo1satBcqP5iiKUjaa07R7EK7eDpU3cHLtuWxr/frxa3gqAINbBWKnlbd3IYQQtzb5SyiEENfI2bU5t4fcXm40J+5wJmmx+Wh1atoOrCTT2hlK5J8UJJ1Zh9OrD+sjbetz7msr6fiFEEIICXSEEOIaiMmNYW1sJaM5VoVdZ0ZzWtwWXPm+OQAluRTt3oPFoEHj7spaXRAmi0LTQFeaBFaSnU0IIYS4xUigI4QQ18C3h79FQeG24Nto5Nmo7HjUvjSyTxdh56Cldb+QCzdwaj358XYAuPQfyK8HUwC4v52M5gghhBBwmckIhBBCXLpiUzFHM4+yP31/paM5FouV3StjAWjdLwR7J90F21IiVlGQZFufk92hBxFb87HTqBncKrAGr0AIIYS4cUigI4QQNUBRFFKKUghPDyc8I5zw9HBO5pzEoljKyvQN7Utjr8ZlP0duTyE/owQHFx0tev9HemizkaItm7Ea7dF4uPGLyRtIpG8TP9wd7WrwqoQQQogbhwQ6QghRDUwWE5HZkWWBzaH0Q6SXpFco5+/kTyufVrTybcWw+sPKjpuNFvb9aRvNaTuwNnb2//H2HL+d/BgrAE79B7D8sC3b2r2yd44QQghRRgIdIYS4AunF6cw/Np+lJ5dSbC4u95xWpaWRZyNa+baipW9LWvm0wt/Jv9J2jmxOpijPiLOHnmbdg/7znNZjq8qyrZ1o3Incgyb8XPX0qO9TPRclhBBC3AQk0BFCiMuQXJjM90e+Z/mp5ZisJgDc9G5lozUtfVrSzLsZDtoL7IFzHmOJmQNr4wFof1cdNLr/yBOjKBT9sxarSY3W05Ufi9yBLIa2qYVGraqGKxNCCCFuDhLoCCHEJYjNi2Xukbn8GfNn2Xqb1r6tmdBiAl0Du6JSXXqwEb4hkdIiE+5+jjTqVPmIT5m0o+RHFgKOaG7vz+aoLADuayvT1oQQQojzSaAjhBBVcDz7OHMOz2Fd/DoUFAA6B3Tm0RaP0s6v3WUFOAAlhUbC1ycA0OHuOqg1/53133rkDwqTbdPWdtbugDUK2oV6EObjfFnnF0IIIW5WEugIIcR/OJRxiDmH57A5aXPZsV7BvZjQfALNfZpfcfsH/krAVGrBO9iZem18L1q+6O+VWM1qtF6ufJ/jCJRwnyQhEEIIISqQQEcIISqxN3Uvsw7NYnfqbgDUKjX9Q/szvsV4Gng0qJZzFOYYOLIpCYCOg8JQXWyNTV4y+YdSAEcMXXsTnVmCg07DnS1k7xwhhBDi3yTQEUKIf1kXv47JmyYDtsxpd9e9m0eaP0Koa2i1nmffmjgsJisBdd0IbeZ10fLWI39QcNo2bW1tUAdIgYHN/XHWy1u5EEII8W/y11EIIc6TZ8jj3V3vAjCg9gCea/scgc7VP2KSl1FM5LbTAHQaElalNT6Fa5ahmNVovZz5IcsRsHBf2+Bq75sQQghxM5BARwghzvPh3g/JLs0mzC2Mad2mYaexq5Hz7FkZi9WqENLUk8D6HhevYCggf280oCe7bRcKjRaCPR3oWMezRvonhBBC3Oj+O72PEELcQnYk7+CP6D9QoeKtLm/VWJATfzSLk3vTANvanKqwHllNYbIOgCXenQC4t00watk7RwghhKiUjOgIIQRQbCrmrZ1vAfBg4wdp5duq2s9RlGtg+9IoovalA1CvrS++oa5Vqlu46mcUixqNlxPLilxQqWFY26Bq76MQQghxs5BARwghgJkHZ3K66DSBToE83frpam3barFyZFMyu1fGYCq1oFJBs1616DS4aqM5WMzk7zgKaElp2hpUKrrW9aaWh2O19lMIIYS4mUigI4S45YWnh7MochEAb3Z+E0dd9QUQqTF5bP7pBJmJhQD41nal14MN8QlxqXIbluMbKUzSAPCtW1cA2TtHCCGEuAgJdIQQtzSjxcjUHVNRUBhUdxBdg7pWS7ulhSZ2rogm4kxmNb2jlk5D6tK0W+DF98s5n6JQMO89FKsKtac9O9XeuNhr6d/Uv1r6KYQQQtysJNARQtzS5hyZQ3ReNJ72nrzY7sUrbk+xKhzflcKOZdGUFpoAaNTZn8731MPR9dKTG1i2zSZjQzKg4UjznqBScXfLQOx1mivuqxBCCHEzk0BHCHHLisqJYu6RuQC80vEV3O3dr6i9zKRCtvx0gpToPAA8A53oOaIhgfUvs93cRNI/+hBziR1aX1fe8uwNCtzXVqatCSGEEBcjgY4Q4pZksVqYsmMKZquZXsG96B/a/7LbKso1cHBdAoc3JqFYFbR6DR3uqkOL22qh0VxmFn9FofirR8g9aRsFOj7uFQpOaKnn60yrYPfL7qsQQghxq5BARwhxS1oUuYgjmUdw1jnzesfXUakubT8axaqQdCKHY1uSiTmUiWJVAKjb2oeu99XHxdP+ivpn3f09Kb/HADrc7uzL9yU+QC73t6t1yX0VQgghbkUS6AghbjmJBYl8Gf4lAJPbTcbPya/KdUsLTUTuTOHY1mTy0kvKjgfUc6PtgNqENvO68g7mJpL16TSM+Xo0rg4UPfE/Ds49hEatYkhr2TtHCCGEqAoJdIQQtxRFUXh759uUmEto79+eYfWHValOakw+R7ckEb0/A4vZCoDOXkOjjv407RGEV5BzdXUQw3cTyDxqm7LmP+Ud3j6YBUDvhj74ulzZSJEQQghxq5BARwhxS1lxagW7Unah1+iZ2nkqatWF19AYS8yc3JPK0S3JZCUXlR33CXGhWY8g6rXzxc6+et9GlX3zSVl+Eqx2OHdpz4kmnVg6axcAE3vXq9ZzCSGEEDczCXSEELeMjOIMPtr3EQBPtnqSENeQSsuZjBZ2/naKyF2pmA0WALQ6NfXb+9G0RxC+oS41s04mN5HcL9+iJNMetV6H19vTGf/rMQBGdAimTYhH9Z9TCCGEuElJoCOEuGVM3zOdAmMBTbyaMKrJqAuW2/ZrFBFbbRt9evg70rRHEA07+mPvpKu5zikKpkVPkL7fNmXN5/kXWBRj4ERaAZ5Odvyvf6OaO7cQQghxE5JARwhxQzqefZzMkkwctA44ah1tX3WOZd9r1OU31Fwfv5518evQqDS81eUttOrK3/5iwjNsQY4KBjzajLDWPlcly5myfz6py45iNTvg0LQhpXcN5bMZ2wB4eWAjPJwufbNRIYQQ4lYmgY4Q4oZzLPMYD65+EKtivWAZvUZfLgBKLUoF4OFmD9PIs/LRkaI8AxsXHgegdZ8Q6rbxrf7OVyY3kYI5UylMdgCNGv/pH/Lc6hMUGy20C/Xg3jayQagQQghxqSTQEULccGYfno1VseLj4IOjzpESUwnF5mKKzcVlwY/BYsBgMZBjyCmrV8etDo+1fKzSNhVF4Z8FxyktNOFVy5mOg8KuyrWgKFh+nUTqbtuIjdejj7JTcWfN0Sg0ahXvDGmGWi375gghhBCXSgIdIcQN5VTOKf5J/AcVKub2m0uY+7mARFEUjFYjxaZiSswlFJtswU+JuYRScynNfZqj1+grbffo5mQSjmWh0arp+3ATNLoLZ2OrVgcWkL7iIJZSJ+xCgnAeP4EpX+8GYFyX2jQOcL06/RBCCCFuMhLoCCFuKN8d/Q6APqF9ygU5ACqVCr1Gj16jx4OqZyjLTili+2+nAOg8tC5egdW0J87F5CZSvOBNcqOdAAiYNp1ZO5NIyC7G39WeZ/s2uDr9EEIIIW5CV+mWpRBCXLmkgiTWxK4B4JHmj1RLmxazlfXzIrCYrAQ38aRFr6u0HkZRsC5/ipQdtkxu7vfdR1qdJnyzKRqAN+9ugrNe7kUJIYQQl0v+igohbhg/HPsBi2KhS2AXmno1rZY296yKJSOhAHsnHbePbozqaq2HOfgjmSv3YSxwQevlic8LzzNu6VGMFis9GvgwsJn/1emHEEIIcZOSER0hxA0hsyST5VHLARjffHy1tHk6KocDf8UD0Ouhhji5V75+p9plRlH68xtkRdqmyPm9OYW/4ovZGpWJnVbN24OaXpWU1kIIIcTNTAIdIcQNYUHEAoxWIy19WtLOr90Vt2coMbNuXgQo0LhLAHVbX4VU0ooCBxehfNOT1O1aUFQ433Ybqp69eXvVMQCe6FmX2t5ONd8XIYQQ4iYnU9eEENe9PEMeS44vAeDR5o9Wy2jHlp9PUJhtwNXbnm7317/i9i6qJBdWPYf10DJS97lTkmWH2skJ/ylv8v76KNLyDYR6OfJEr7o13xchhBDiFiCBjhDiuvfz8Z8pNhdT36M+PWr1uOL2ovamcXJ3GioV9H24KXb2NfxWGL8Tlj2K6fRpkrb5UJqjA7Ua/6lTiLI48MOOOADeGtQUe52mZvsihBBC3CIk0BFCXNeKTcUsjFwIwPhm4694NKcgu5TNP50AoO0dtfEPc7viPl6QxQxbPoItH1KUoiV5lx+WUtB4eBD02ac4dOjIG7N2YrEq3NHcn14Nr8L0OSGEEOIWIYGOEOK69lvUb+Qacgl2CaZf7X4AlBaZWP31YaxWheDGnoQ08cSvjitqzX8vO1SsChvmR2AoNuNb25V2d9SuuY7nxMOyR1ESdpN93In0w26ggH3TptSa+QW6wECW7E1gf3wOjnYa3rirSc31RQghhLgFSaAjhLhumSwmfjj2AwDjmo1Dq7a9ZR3dnERKdB4AabH57Fsdh529hlqNPAluYgt8XL0dKrQXviGR5BO5aPUa+o5rguYigdFlO7LUth6nsIDT+30oiLPtleN2zz34T3kTtb092UVGpq85DsBzfRoQ4Faxv0IIIYS4fBLoCCGuWytjVpJenI6Pgw+D6w4GwGyycHhjEgDNegZRWmQiMTIbQ5GZmPAMYsIzAHDzdSCksSfBTb0IauBOfmYJu363bcbZ/b76uPs5Vn+HDQWw5iUIX4SxQEPS7mAMmWbQavF/7VXchw8vm3r34drj5BabaOTvwtiutau/L0IIIcQtTgIdIcR1yWK18P3R7wEY03QMdho7AE7uTqOkwISzh55u99dHo1FjtSpkxBeQGJlFQkQ2qTH55KWXcCQ9mSObk1FrVOj0GqxmhTotvWncNaD6O5y8H34bD9kxFJx24PReX6wlJjQ+3tT6/HMc27QpK7ouIo2f9yYC8M6QZuhqamRJCCGEuIVJoCOEuC6tS1hHfH48rnau3NfgPsC2xiZ8fQIALW8PLpt6plar8Kvjil8dV9rdUQdjiZmkEzkkRmSTEJFFfmYphmIzDq529H6oUfVuxmkotCUc2PklisVM5qkgMg8AigmH1q0JmjEDnZ8tyYCiKHy/PY5pf0YA8EC7YNrX9qy+vgghhBCijAQ6QojrjqIozD08F4CHGj+Eo842zSz+aBY5qcXY2Wto0jXwgvXtHLSEtfIhrJUPAHkZxZyOysM/zBUHF7vq6iRE/A5/vQr5yViMKk5HNKHweC4A7iOG4//KK6jsbOczmq1M+eMoP+2xjeTc364W7wxpVj19EUIIIUQFEugIIa4725K3cSLnBA5aBx5s/GDZ8YPrbKM5TbsHYedQ9bcvNx9H3HyqcU1OZhSsfhFiNmIxqsjPCiY7yhPj6QxUdnb4T5mC+7ChZcVzi408vnA/u2KyUang1YGNGd+9TvWOLAkhhBCiHAl0hBDXnblHbKM59ze4Hze9bZ+btLh8TkflolaraHFbrWvTMWMRbPkYZdtMCk+ryYvzojDFEcVkATLQBgRQ64svcGh+bqQmOqOQR37YS1xWMU52Gr4Y0ZrbG/tdm/4LIYQQtxAJdIQQ15X9afs5kH4AnVrH6Kajy46fXZtTv70fzh72V7dTioISuZLSha+QdzSf/HhPLAbNmSct2NWti9vgwbjfdy9aD4+yalujMpi46AAFpWaC3B34bmw7Gvm7Xt2+CyGEELcoCXSEENeVs6M5g+sNxtfRtog/P7OE6P3pALTqG3xV+2OK2EXezJfI25+MMV8HOAOg8fTE9a47cRs0GPumTSpMQ/txZxxTV0ZgsSq0CXFn9uh2eDvrr2rfhRBCiFuZBDpCiOtGZFYk25K3oVapebjpw2XHD/2TiKJAcGMPvGu51Hg/FEUhf8Uycn+YSfGJVEAF6FBp1bj06YPrkHtw7toVlU5Xoa7ZYuWdVRHM3xkPwD2tg5g+tDn2Ok2FskIIIYSoORLoCCGuG98d/Q6AAbUHEOxqG7kpLTIRsT0FgFZ9Q65KPzKmTyFrwa9nflLhGOKI24PjcRn2EBqXCwdaeSUmJi0+wNaoTABe7N+Qib3qStIBIYQQ4hqQQEcIcV2Iy4vj77i/AXik+SNlx49tTcZssOAV5ERw45rfcyZ30XdlQY5XK3B//CXseo6BiwQrcZlFPDJ/L9EZRTjoNHz2QEsGNKuBjUmFEEIIUSUS6AghrgvfH/0eBYVetXrRwKMBABaTlcMbkwDbaE5Nj4wUbVlPyrSPAfBqq8N39kZw8gLOTGcrMXM6r4SUvBJO55ZyOreElDzb12On8yk0mPF3tWfumHY0C3Kr0b4KIYQQ4r9JoCOEuObSitJYGbMSKD+ac3JvGsV5Rpzc7KjfrmZTMhtPHiP56afBCi5hsGXct+xbm0xy7ilS8kpJyS2hyGj5zzZa1HJjzuh2+Lle5axwQgghhKhAAh0hxDW36PgizFYzbf3a0sq3FWAbQTmbUrrFbcFotOoaO78lK53EMSOwlCrY+1j56d73+Gp9DpBToayHo44ANwcC3R0IdLc/8709tTwcaFnLHa2m5vophBBCiKqTQEcIcU0VmYpYemIpAGOajCk7nhCRTfbpInR6DU27B9bY+ZXSUpJG3okxx4TOycqBCa/z1TF7NGoVE3qEUcfbiUC3c0GNg51kTxNCCCFuBBLoCCGuqeVRyykwFVDbtTY9g3uWHQ9fZxvNadItEL1jxTTO1UGxWkl55A6K4wpRa61kPvUUzx+zJTx4e3BTRnYMrZHzCiGEEKLmSaAjhLhmzFYzCyMXAjCqySjUKtu0r4zEApKO56BSq2hxW62KFU2lsPoFsHOGLpPArZIyVZD18gjy9qeASkH1xEhGR9UFrEzoESZBjhBCCHGDk0BHCHHNbEjYQHJhMu56d+6ue3fZ8bNrc+q19cXVy6FixUM/wcEfbd/vnQutH4Juz4FH1YOT/M+fJeOPwwA4j7idQVmdMFpMDGjqz8sDGl3+RQkhhBDiuiCrZoUQ14SiKCw4tgCABxo+gIPWFtAUZJdyam86AK36BFdWEWXPHHJjHMhNrYWl1Az758HMNvD7JMiOvei5S379iNOz1wLgcltTRjveR06xiZa13PjsgVao1bLBpxBCCHGjkxEdIcQ1EZ4RzuHMw9ip7RjeaHjZ8cMbk7BaFYIauuMb6lqxYuJuCg+eImWPF2AlVReMc5g9rt4JOJt+RB2+GFoOh+7Pg1fdCtWNWxaR+O4cFIsGpyZ+TG76DLGJ+QS5OzBnTDtJNiCEEELcJCTQEUJcE/OPzQfgrrp34e3gDYChxMyxrckAtOoTUnnFPXPIPuEMgNrVFWt+PgUnCik44Ylar8YlsBDXlKU4HfwJVcv7occL4F0fAMux9ST9byoWgxZ9gDNfDJ7GnshcXPRa5o1rj6+L7H8jhBBC3Cwk0BFCXHUJ+Qn8k/APAKObjC47HrHtNKZSCx4BToQ29apYsTCd0h2rKE73BLWasBXLsRQUkL9qFXl//on5dAp5sY7kxTqisbfgemA1bv8sw77n3dDoLpKffQlDrhats5a1j3/K0oO5aNUqvnmoLQ38XK7W5QshhBDiKpBARwhx1f0Y8SMKCt2CulHX3Ta9zGKxcvifRMC2NkdV2TqZ/fPJOa4HwKVvX3SBgegA+4YN8XnuOUoOHiRv1SoK1qzFkptLTpQzOVHO6HZuRee8ieI0PSqdiqgXP+LDg/kAvDukGd3qe1+V6xZCCCHE1SOBjhDiqsoz5PF79O8AjGl6boPQU/vSKcwx4OBqR8MO/hUrWsyYt39PXrwjAJ5jRpd7WqVW49i2LY5t2+L/6qsU7dhB3qo/KVj3N6YiMBXZ3u6Kn3+NZw5bAXiiV12Gd7jAFDkhhBBC3NAk0BFCXFW/nvyVEnMJDT0a0tG/I2DLwHY2pXSL3rXQ6CpJCHlyDbkH81Esrtg3acIuh0AKwpMJ83amjo8Tzvpzb2cqnQ7nnj1x7tkTa/FUCjZupPDvNRS17szYOG9MFjN3Ng/gxX4Nr8o1CyGEEOLqk0BHCHHVmCwmFkcuBmyjOSqVbXpaYkQ2mYmFaO3UNOsRVGldZedsck45AZDR/x4enr+/3PN+rnrq+jgT5uNEmLfta10fZwLdHXC7807MPfsw4usd5JcW0zrEnU/ubylppIUQQoib2GUFOl999RUfffQRqamptGzZkpkzZ9KhQ4dKy86ZM4cFCxZw9OhRANq2bct77713wfJCiJvX6tjVZJRk4Ovgy4DaA8qO71sTB0DT7kHYO+kqVsw4Sf7WPZhLPNF4evCZOQQooJaHA6UmC5mFRtLyDaTlG9gRnVWuqp1WTR0vJ0rNFhKyiwn2dGDO6HbY6ySNtBBCCHEzu+RAZ8mSJUyePJlvv/2Wjh07MmPGDPr378+JEyfw9fWtUH7Tpk2MGDGCLl26YG9vzwcffEC/fv04duwYQUGV37kVQtx8FEVhfoQtpfSDjR9Ep7EFNKejckg5lYdaq6J13wusl9k7l5yTtpTSxXcMZWdiAXYaNb8+3pkANwfyik1EZxYSk1FETIbta3RGIfFZxRjNVk6kFQDgYq9l3tj2eDvra/6ChRBCCHFNqRRFUS6lQseOHWnfvj1ffvklAFarleDgYJ566ilefvnli9a3WCx4eHjw5ZdfMnr06ErLGAwGDAZD2c/5+fkEBweTl5eHq2slGwgKIa57O07v4LF1j+GgdWDdvetw07sB8McX4SRGZNO0eyC9RjaqWNFQSMkrTYlb7YhKq2XGY5+yNtnIiA4hTB/a/D/PabEqJOUUE5NRRHxWEZ3qetHIX95DhBBCiBtZfn4+bm5uF40NLmlEx2g0sn//fl555ZWyY2q1mj59+rBz584qtVFcXIzJZMLT0/OCZaZPn85bb711KV0TQlznFhxbAMA99e4pC3LS4vJJjMhGpVbRpn9o5RWP/EK2beYrlt59WZtsRKNW8UTPuhc9p0atItTLiVAvp2q5BiGEEELcOCpJbXRhmZmZWCwW/Pz8yh338/MjNTW1Sm289NJLBAYG0qdPnwuWeeWVV8jLyyt7JCYmXko3hRDXmaicKLaf3o5apeahJg+VHd9/Zm1Og/Z+uHo7VKyoKJj+mU1+ou25n0K6ADC4ZSAhXo413m8hhBBC3Liuata1999/n59//plNmzZhb29/wXJ6vR69XubQC3GzWBBhG825PeR2gl2CAchKLiT2UCaooM2AC4zmJOwkZ1cSKC7QrBkLsxxQqWBi74uP5gghhBDi1nZJIzre3t5oNBrS0tLKHU9LS8Pfv5IN/s7z8cf/b+++w6Os0jeOf2cmmUlvpEESEiChhBqqgA1EEUQRULGwuNh1UVddXcsKuhbsir3suorK2qUpRRAUkB6ClBA6CYT0Xqe9vz+iUX4JK5EUCPfnunIpM+eceQ4aLu6c933e53jqqadYsmQJvXr1anilInJKyqvM4+t9XwMwOfHX+/I2LToIQKekMELa1n9pmXv1WxTtrTm5WZxYcwp8YfdI4sP9m7JkERERaQUaFHSsViv9+vVj2bJlta+53W6WLVvG4MGDjznvmWee4bHHHmPRokX079//j1crIqec2amzcbgd9A7rTZ/wPixPlVMAAEU2SURBVAAU5VSwZ2PND0z6XRhX/8TSbEoWL8VVbcEU1oZXq9sC8Jdh8c1QtYiIiJzqGnzp2t133821115L//79GThwIC+99BLl5eVMmTIFgMmTJxMVFcWMGTMAePrpp5k2bRqzZ88mLi6u9l4ePz8//Pz8GnErInKyqXBU8OmuT4GaB4T+YvPigxgGxPZoQ1j7+k9njE3vUZBWc2/O+r4X4jRZGNYljB5RgU1fuIiIiJzyGhx0Jk6cSG5uLtOmTSMrK4s+ffqwaNGi2gYF6enpmM2/HhS98cYb2O12LrvssqPWmT59Oo888siJVS8iJ7V5e+dRXF1MtF80w2OGA1BaUMXOtTU/8Og3Kq7+iS4nFfPfo7rIE6yevGCtaTs9dbhOc0REROT4/KFmBFOnTmXq1Kn1vrdixYqjfn3gwIE/8hEicopzuV18sOMDACYlTsJitgCw+dt03C6DqM5BtO10jNOZtK8pSKkEvNmTdA5FFm8Gd2xDv9hjt6UXERER+a0G3aMjInK8VhxaQXppOv5Wf8bFjwOgosTOjlWZAPQbHXfMufYlr1N2uKYz44uB/QCd5oiIiEjDKOiISJP45QGhV3S+Ah/Pms5pW5al43K4iegQQHSX4Pon5qZRuHwHYCK/cyL7fMLoExPEkE5tmqlyERERaQ0UdESk0aUVpJGck4yHyYOru10NQFW5g63fHwZq7s0xmUz1znWtfJOifTXB6M2IMwG4fXj8MceLiIiI1EdBR0Qa3Vd7vgJgWPthhPuEA7B1xSEcVS7aRPkR1/MYpzPVpRR/NQe3w0x1mxBWh8TTrW0Aw7uGN1fpIiIi0koo6IhIo7K77CzYtwCg9t4ce5WTLd9lANBvVOwxT2eMlI8p3FnTI+XDuOEYJjN/GdZJpzkiIiLSYAo6ItKovsv4juLqYsJ9whnSbggA2344THW5k6AIHzr1PcbpjGFQ9tmb2Es9cNs8WdA2iY5hvozq0bYZqxcREZHWQkFHRBrVV7trLlsb22ksFrMFp91FytKa05y+I2Mxm49xOnPwRwo3FACwrOMZVHnYuO3ceCzHGi8iIiLyPyjoiEijOVJ2hDWZa4BfL1tL/fEIlSV2/EO86Dwo4phzq7+eSXm2F5jgw+iziA72Zmyfds1St4iIiLQ+Cjoi0mjm7J2DgcGAyAHEBMTgcrlJXnIQgKQL2mOxHOOPnOJDFCxcD8DO6ARyfEO45ZxOeB5rvIiIiMjv0N8iRKRRuA03c/fMBX49zdm1Louygmp8Aqx0G3rse22cXz9G8X4bAO/GjiDc38Zl/aKbvmgRERFptRR0RKRRrM9az+Gyw/h5+jEidgRut8GmRTWnOX1GtMfD01L/xLw95H+yEMNlJrtNOFvbdOSmszvidazxIiIiIsdBQUdEGsUvTQhGdxiNt4c3ezflUJxTic3Xg+5nH/teG8ec6RTurnlA6MudxxLsa+XqQe2bpWYRERFpvRR0ROSEFVcXs/TgUgDGJYzDMAw2LToAQO/hMVi9POqfmLWV3C9XYrhNZETGkBzemevP7ICP9RjjRURERI6Tgo6InLCF+xdid9tJCE6ge5vuZOwoIP9wOZ42Cz3PPfa9NtWf/oPi/TWnOS90Hou/lyd/GhzXTFWLiIhIa6agIyIn7MvdXwIwPn48JpOJLctqnpuTOLQdXr6e9U9KX0fu/C1gmEiNTmBnSBx/GR5PoPcxxouIiIg0gIKOiJyQnQU7SS1IxcPswUUdL6Igs5z0HQWYTNBr+DFOcwyDyo8eojTDGwOY2fkS2of4MGVoXHOWLiIiIq2Ygo6InJBfmhAMjxlOsFcwW76rOc3p0CeMgFDv+iftW07u4r0ArGvfg4MBbXlgVFdsHuq0JiIiIo1DQUdE/rBqVzUL9i0AapoQVJbZSVuXBdQ0IaiXYVD+n39QnuWFYTbxVpeLGdghhAt7RDZX2SIiInIaUGsjEfnDlqcvp8ReQoRPBIPbDmbzogxcDjdh7f1pGx9Y7xwjdQE5y7MBK9/G9iPbrw3/GpOIyWRq3uJFRESkVdOJjoj8Yb80IRgbPxbcJrZ+fwiA3ufF1B9c3C7K/v0IVflW3B5m3usymsv6RtMjqv5QJCIiIvJHKeiIyB+SWZbJ2iNrAbg0/lL2bMymotiOb6CV+H7h9c4xtnxK7qpiAOZ2HEp1QDD3juzSbDWLiIjI6UNBR0T+kLl75mJgMDByINF+0aT83FK657BoLB71/NHitFPyzhNUF3visHrwUfwF3HZuJ8IDvJq5chERETkdKOiISIO5DTdz9swBapoQZO4uIi+jDA9PM93PjKp3jrHhPXLXVQPwcfxwgsJDuOGsjs1VsoiIiJxmFHREpMHWHVlHZnkm/p7+jGg/ovYBoV3OiMTLr54HfjoqKXz7eRzlHlR62fii47ncP6orXp5qJy0iIiJNQ0FHRBrsl2fnjO44mupCN/t/ygNqmhDUx73ydfI2GwC81+VCenSKYEyvts1TrIiIiJyW1F5aRBqkuLqYZenLgJrL1n5aeggMaN+9DcGRvnUnVJVQ8K83cFV5UuLrwzexg/lc7aRFRESkielER0Qa5Ot9X2N32+kc3JlO3p1J/fEIAH2OcZrjWvYi+VtrLlF7q+slXNIvlj4xQc1VroiIiJymdKIjIg3ySxOC8QnjSV19BEe1i5B2vkR3C647uDyf/Hffx+2wkRsYyJoO/fnuQrWTFhERkaanEx0ROW6p+amkFqTiafZkVPvR/LS8pglB7+H1PyDU8c2TFOy0AvBat3HcfG4CbQO9m7VmEREROT0p6IjIcfty95cADG8/nIKdDsoKqvHy86TzwIi6g0syyZv1JYbLxKGQMNI79+Pmszs1c8UiIiJyulLQEZHjUu2q5uv9XwMwPn58bUvpHmdH4WGt2yba/uWjFO2xAfBS4mXcN6or3vWMExEREWkKCjoiclyWHVxGqb2USN9I4qq7kbWvGLOHiR7n1POA0OLD5P53CRgmdkVE45HUj7G963+QqIiIiEhTUDMCEfldTreTz3Z9BsCl8Zey7bvDAHTuH4FvoK3O+Op5L1BysOb1l7tdzjNjumE2q520iIiINB+d6IjI/7QxayNXLLiCjdkbMWHigjaj2ZOcC0Cv+lpKOyrJ++98wMTudtEknjOAfrEhzVu0iIiInPZ0oiMi9cqtyOX5Tc/z9b6a+3ICbYHcP/B+cje4MNwGUZ2DCIvxrzOv+tu3Kdlf8zOUNxMv4221kxYREZEWoKAjIkdxuB3MTp3NG1veoNxRjgkTEzpP4M6kO/E1+fP+q6sB6F3faY5hkPfOf8AwcaRtG0aMHUZ0sE8z70BEREREQUdEfmND1gaeXPcke4r2ANAztCcPDXqI7qHdAdi64hDVFU4Cw7yJ6xlaZ7593TxKdlYBJv7b90pePkftpEVERKRlKOiICNnl2Ty/8XkWHlgIQJAtiL/2/SvjEsZhNtVchma4DbZ8V9NSutfwGEz1NBfIfulZMEyURPgw7s8T1E5aREREWoyCjshpzOFy8GHqh7y55U0qnBWYMHFFlyu4Pel2Am2BR409uC2f4pxKrN4edB0cWXettGTKfsoDTKw8ayL39Kg7RkRERKS5KOiInKaSs5N5ZM0j7C/eD0CvsF48NOghEtsk1hnrcrlJXnIQgMQz22H1qvtHx8EnHgK3CUe4B2Om3oLJpHbSIiIi0nIUdEROQ+kl6dz87c1UuaoI8Qrhr33/ytj4sbWXqf1WSV4lS/69nez9JZjNJnoNi64zpjL9AI6N+wETB8+/mIsjA5phFyIiIiLHpqAjcpoxDINH1jxClauKfhH9eHn4ywRY6w8mezfnsPyDnVRXOLF6e3Detd3wD/GqM27HtHvxcZuwhLo5668PN/UWRERERH6Xgo7IaeaL3V+wIWsDXhYvHhv6WL0hx+lwsfrzPWz7/jAAER0CuOD67gSEetcZm3PgMD4btgFQeeFwgvzrjhERERFpbgo6IqeRX7qrAdyedDsx/nWfhVOYVc7if20n/1AZAEkXtGfQ2I5YLHUvawNImf4QMS6whThI+OsTTVe8iIiISAMo6IicJgzD4PF1j1PmKKNnaE+u6XZNnTE71xzh+4934ax24e3vyXl/TiS2e5tjrrll2wGiNq4HwDayLx5+IU1Wv4iIiEhDKOiInCYWH1zMiowVeJg9eHTIo1jMvz7jxl7l5If/7iJtXRYAUV2COH9Kd3yDbMdcz+02SJ7xNGe4DLyC7bS7eXpTb0FERETkuCnoiJwGiqqKmLFuBgA39ryRhOCE2vdyM0pZ/M42inMqMZlgwJgO9BsVh7meB4L+1pxVafTf8gMAgWfHYIrs3nQbEBEREWkgBR2R08AzG56hoKqA+KB4buh5A1BzKdvWFYdZ/cVu3E4D3yAbF1zfnXYJQb+7XmmVg7RX36Cb040t0EHwtfc08Q5EREREGkZBR6SVW3V4FfP3zceEiUeHPIrVYsUwDJa9l1p7qVpcr1DOm9wNLz/P41rzjW9+YlTqdwC06W/D1HV0k9UvIiIi8kco6Ii0YuWOcv655p8ATEqcRK+wXgDsTc4lbV0WZrOJIRPi6TU8GpPpf1+q9os9OWUUfPQRNocTq7+DgKtuh9/c7yMiIiJyMqi/X6yItAozk2dypPwIUX5RTO0zFahpPLD6890A9L0wlt7nxRx3yDEMgxlfbuay3csBCO1lx9RvctMULyIiInICFHREWqnNOZv5eOfHAEwfPB0fTx8ANi08SFlhNf4hXvS9MLZBay5NzSHw23n42qvw9HMScMk48A5u9NpFRERETpSCjkgrVO2qZvqP0zEwGBc/jsHtBgM1DwNNWZoOwJlXJOBpPf5LzqocLp6ak8IVe34+zUksxTT41sYvXkRERKQRKOiItEJvbXmL/cX7CfUO5Z7+NR3RDMNg5ae7cbsM2ndvQ4feoQ1a89+r9tN98woCq8vx9HESOGwghHdtivJFRERETpiCjkgrs7NgJ//Z9h8A/jHoHwTaAgHYl5JLxo4CzB4mzpqYcNz35bjcBq8t38Mri3Zw+e6fO60llmEafEvTbEBERESkEajrmkgr4nQ7mbZ6Gk7Dyfmx53Ne7HkAOOwuVn1W04Ag6fz2BIX7HNd6h4squeuTFNbvL2D0gXWEVpXg4e0iMCkUOo9ssn2IiIiInCgFHZFWZNaOWaQWpBJgDeDBQQ/Wvr5p4QHKCqrxC7HRb1Tcca214KdMHvxyKyVVTnqUZ3LLwRUAtOlWhnnwHWopLSIiIic1BR2RVuJgyUFeT3kdgHsH3Euod809OEXZFWz+9ucGBJf/fgOCsmon0+du54vkQ2AY3FawiYvXfgEOB55+ToK6GJA0qWk3IyIiInKCFHREWgHDMPjnmn9S7apmcNvBjO00tvb1lZ/uxu00iEkMoWOfsP+5zub0Qu78OIX0ggqC7OW8dHA+Eds3AuDXJYC2iWmY+10H3kFNvSURERGRE6KgI9IKrDy8kvVZ67FZbEwbPK220cD+LXmkb8/HbDFx9sTOx2xA4HIbvL58Dy8t243LbTC8MoN7Nn6EOT8Pk9WT8HODCA7egskEDLypGXcmIiIi8sco6Iic4tyGm5eTXwbg6q5XE+0fDYDzNw0I+oxoT1BE/Q0IDhVWcNcnKWw4UIjZ7eKfRWsZsHIOGAbWyCCi+qXj5X8QPLzggsfVUlpEREROCQo6Iqe4RfsXkVaYhp+nH9f1uK729U2LD1KaX4VfsI3+o+PqnTtvSyYPfbWV0ioncc5Snt/9BT5p2wAI7O5NZLdUzB4GxJ0FF8+ENp2aY0siIiIiJ0xBR+QU5nA7eDXlVQCm9JhCkFcQAMW5FWxeXNOAYOhlCXja6jYgmDZ3G7PWHATgGsc+Jv3wIZSWYPbyJLJvHoHtM8EWCBf8E/peC8f53B0RERGRk4GCjsgp7KvdX5FRmkGIVwiTuv3aCW3Vp7txOd1Edw2mU9+6DQiWp+Uwa81BbG4HMwt+IHbVQgC8IsxEDTiE1c8FXcfA6OcgoG2z7UdERESksSjoiJyiKp2VvLnlTQBu7nUzPp419+Ac+CmPA1t/bkBwZd0GBHanm8fm7yC6NIdnd3xC0JGaU502XcsI61mCKSCsJuAkjtUpjoiIiJyyFHRETlGzU2eTW5lLlF8Ul3e+HKhpQLDy010A9D4vhuBI3zrz3v/xAHmHc3hn1esEVJdh8YZ2A/Pxa1sNfa6paTjgE9KsexERERFpbAo6Iqeg4upi/r3t3wD8pc9f8LR4ApC8JJ2SvCp8g+pvQJBbWs3Ly3Zz/fYFBFSXYQ1wEDssH4+20TXNBjoNb85tiIiIiDQZBR2RU9B729+j1F5KfFA8ozuMBqAkr5LkxTWXoQ29LB6rV91v72cX76T94V2MTN8AQNuBxXgMuwWG/wOsdU9/RERERE5VCjoip5jcilw+Sv0IgDuS7sBirumotuqz3bgcbqK6BBPfL7zOvJ8OFfHVhgO8ueUTAII6luNz1T9g6J3NV7yIiIhIMzH/kUmvvfYacXFxeHl5MWjQINavX3/Msdu3b2fChAnExcVhMpl46aWX/mitIgK89dNbVDor6R3Wm3NjzgUgN72U/VvyMJngrIkJdRoQGIbBI/O2M373CtqV5mOxuQi/uDsMvr0FdiAiIiLS9BocdD755BPuvvtupk+fTnJyMr1792bkyJHk5OTUO76iooKOHTvy1FNPERkZecIFi5zOMkoy+GLXFwDc2ffO2kCzaVHNJWvx/SNo086vzry5KZkcSt3Hn9KWABA+wInl6nfA/Id+1iEiIiJy0mvw33JeeOEFbrzxRqZMmUJiYiJvvvkmPj4+vPvuu/WOHzBgAM8++yxXXnklNpvthAsWOZ29tuU1nIaToe2GMiByAACFWeXs3Vzzg4a+I2PrzCmvdjLjmx08+NMHWFxufMKqCbzzWQiMbtbaRURERJpTg4KO3W5n06ZNjBgx4tcFzGZGjBjBmjVrGq2o6upqSkpKjvoSOd2lFaTxzb5vALij7x21rycvSQcD4nqFEhpd9zTn9RV76J72I12yM8BsEDlpKKaeE5qtbhEREZGW0KCgk5eXh8vlIiIi4qjXIyIiyMrKarSiZsyYQWBgYO1XTExMo60t0lLWHVnHcxue41DpoT80/5XNr2BgcGHchSS2SQSgtKCKXWtrvvf6XVj3NCc9v4IPv0vlr1s/A6BNbwu2a1/7gzsQEREROXWclBfoP/DAAxQXF9d+ZWRktHRJIiekylnFvd/fy/s73ufSuZfy1pa3sLvsxz1/c85mvj/0PRaThalJU399/dt03G6DqC5BRHYMrDPv8a93cP/2/+BZ6cLT10noo6+DV0Cj7ElERETkZNagoBMaGorFYiE7O/uo17Ozsxu10YDNZiMgIOCoL5FT2YJ9CyisLsRislDtqubVlFcZP288Px7+8XfnGobBS5teAmBcwjhiA2pObipK7OxYlQlAvwvj6sxbtTuPoh+X02fvLgAirz0fc+dzGmdDIiIiIie5BgUdq9VKv379WLZsWe1rbrebZcuWMXjw4EYvTqQ1cBtuPtjxAQB397ubp856ilDvUA6WHOTmpTdzz4p7yCo/9qWfKw+vJDknGZvFxi29bql9fct3GbgcbsJj/YnuGnzUHIfLzeNzU3j4p/fBMOHf2Qu/v8xsmg2KiIiInIQafOna3XffzTvvvMP7779Pamoqt956K+Xl5UyZMgWAyZMn88ADD9SOt9vtpKSkkJKSgt1u5/Dhw6SkpLBnz57G24XISWzV4VXsK96Hn6cf4xPGc1HHi5h36TwmdZuE2WRmycElXDLnEt7b9h4Ot+OouW7DzcvJLwNwdderifCtuT+uusLBthU19/r0GxVX57k5H649yORNL+NR4MLsYRDxzJtg8WyG3YqIiIicHBocdCZOnMhzzz3HtGnT6NOnDykpKSxatKi2QUF6ejpHjhypHZ+ZmUlSUhJJSUkcOXKE5557jqSkJG644YbG24XISWzWjlkATEiYgJ+1piuav9Wfvw/8O5+O+ZQ+YX2odFby/KbnuWL+FWzI2lA7d9H+RaQVpuHn6cd1Pa6rfX3r94exV7kIbutLh16hR31eflk1K+d/Sb9taQCEXXU+nl0HNfU2RURERE4qJsMwjJYu4veUlJQQGBhIcXGx7teRU0paQRqXzb8Mi8nC/EsW0MYcjk+A9agxbsPN3D1zeXHTixRWFwIwpuMY7ux7J9ctvo6M0gxuT7qdm3rdBIDD7mLWgz9SVeZgxJREugw6+v646V9s4Kq3J2McBFukNx2+XYfJU6c5IiIi0jocbzY4KbuuibQWv5zmnB97Pj99lM97969m98ajm3mYTWbGJYxj/rj5XN75ckyYWLBvAaO+GEVGaQYhXiFM6japdvyOVZlUlTnwb+NFQv/wo9banllM0pJpGAcBDNo+PVMhR0RERE5LCjoiTSS3Ipdv9tc84HNc4FUc2JqP4TZY+t4ODqUV1hkfaAtk2uBpzL5oNoltEnEaTgBu7nUzPp4+ALicblK+TQeg78hYzJZfv4UNw2DBf/9Fz+TdAARfdDbeg85q0j2KiIiInKw8WroAkdbqvzv/i9PtJCk8idINNZeredgsOKtdLHzjJ8bf2482UX515vUI7cHs0bOZt3ceORU5XN7l8tr3dq3PoqywGp8AK10HH33J2pIN27hyzWtUlNow+9kIm/5c025QRERE5CSmEx2RJlDprOTTXZ8CcGXbyexNzgHg0ruSaBsfiL3KxfyXUygtqKp3vsVsYVzCOG7ufTOe5ppLz9xug+TFNac5fUa0x8PTUjve6XLj+9kdVO6oCVSR/5iORfeziYiIyGlMQUekCczfO5/i6mKi/KLw2RGNYUD77m2IiAtg9K29CG7rS3mxnfkvp1BV7vj9BYF9m3Mpyq7A5uNB97PbHfXehh8W0n7jAQy3Ca+kHgSMvbQJdiUiIiJy6lDQEWlkv31A6DVx17JzTc3DQPte0B4AL19PLr69N75BNgqzKvjm9Z9w2l3/c03DMNi06AAAvYZFY/U6+qpT//mPU37EC8ME7Z58ps5zdURERERONwo6Io1s5aGVHCg5gL+nPx0y+uJyuAmP9add56DaMf4hXlx8e2+s3h4c2VvMt+/uwO0+dqf39O0F5GWU4WGz0GtYzFHv7Vu/kKCtNc+u8jrvXGwdOjTJvkREREROJQo6Io3sl5bSl3W4gp0ra1pJJ10QW+eUpU2UH6Nv6YnZw8S+lFxWfrKLYz3W6pfTnB5nR+Hl95t20YaBMfeflB7yAqDd7X9t3M2IiIiInKIUdEQaUWp+Kuuz1mMxWRhYcgHV5U4CwrzpmBQG696CWWOh5Ejt+KguwZw/pTuYYNv3h0lefLDOmpm7iziypxizh4k+I44+zSnathifLYfBMGH07YtXly5NvkcRERGRU4GCjkgj+uXenAvaj2TfD0UAJI2IwVx0ABY/BPtWwIK74DcnN/H9wjnz8gQA1s7Zx841R45a85fTnG5D2uEbaPv1DcOg/KtHKN5X84yd2L/e2SR7EhERETkVKeiINJLs8mwW7l8IwIXuKyjNr8Lb35Oug9vC8ifA/XN3tV0LYdsXR83tPTyGpPNrmhUs/2AnB7fnA5CbXkr69gJMZlPt+7+wpy7EM+UQhttEdcd4fAYMaOIdioiIiJw6FHREGsnHaR/jNJz0DetL7pqaLmo9z43GI387bP0Mt8OEve1FNYMX3gfleUfNHzyuE50HRuB2Gyx6exs5B0tqT3MSBoQTGOb962DDoHTeoxTu9gUg9o6p6rQmIiIi8hsKOiKNoMJRwadpNQ8IneA7uaZDmtVMz3OiYdmjGAakb+zM3pdSKC3vDBX5NWHnN0xmE8MndyO6azDOahfzX97C3s25APQdGXvUWCN1HqRk4HaYqQyPJOj8Ec2zUREREZFThIKOSCOYt3ceJfYSYvxjMDaHAJA4tB1euWtgz1JKj/hQebAUDIOMlRZcDo+ay9d2fnPUOhYPM6Nu7klojF/Ng0QN6NA7lDbt/H4d5HZTsfAxCtJqXou86SZMFkuz7VVERETkVKCgI3KCfvuA0Cvb/JlDOwsxmU30Pi8avp2O4YYjO6MAcJnMmIqKyUz/+X6aBXdBZdFR61m9PRgztTcBoV6YzCb6j447+gO3f4njp0M4Ky1U+gUSefn4Jt6hiIiIyKlHQUfkBH2f8T3ppen4W/0J213T3jm+XzgB2UsgM5niw0G4c8op9fTm0TOm4MZE2YaDlJV1gLIsWPKPOmv6Btq44qGBXD19EOGxAb++4XJiX/oEBTtrTnN8r56E2WarM19ERETkdKegI3KC3t/xPgCXt72a/ckFACSdFwXfPYbhgiM7QgGY23U4N/5tEnM7nQXAwdVeuOwm2PwB7F1eZ12btwdBET5Hv7j1M6p3HKa62JNqqxfxN0xuwp2JiIiInLoUdEROwPa87WzK3oSHyYNuh8/EcBvEdAsmLO8ryN9D4aEwKKygwOZP6J/+xAXdI7HdchuZvm0wFxaTmfHzJWzz74Dqsv/9YS4H7hVPkf/zaY5j9KVYAgL+9xwRERGR05SCjsgJmLVjFgCj2o3h4LoiAJKGR8KKp3A7TWRtrQkic3qM5LrzEwG4fVRPvhrxZwDKNhyirDQKitLhu8f+94dt+S9Vew5TmWvDabbQ686bm2RPIiIiIq2Bgo7IH5RVnsWSA0sAGFo4BqfdTWiMH9GFs6Esi4KMKExllWT5BBN/3TUEensCYPUwc/tdE/m641AADvwYgMthgnVvQfra+j/Macf4/hnyU2tOcwqHnoetbWTTb1JERETkFKWgI/IHFFcX88yGZ3AaTgaEDiJrnR2Avue2wbR6Ji67ieyfaoLNvD4XMfmshKPmd4n0J/ivd5HlE4ylsJjMjL6AAXOngqOq7gdunoU94whlh2seGtrr7r806f5ERERETnUKOiIN4HA7mJ06m4u+uohvD34LwFjnZKrKHPi38aJTyX+gupi8jA6YK6s54B9B3xuuwtta9zk3fx6RyKJR1wNQtv4I5aURkL8bvn/6/31oFfzwPPmpvgBkdh9AULfOTbtRERERkVOcgo7IcVp5aCUT5k1gxvoZFFcXEx8Uz1vnvUXJxpqTmz5D/TFvfBtnlZm8LS4Avhk4lokD4+pdz2I2MfWeq1jScTAAe9eE4HaaYPVMyEz5deCm/+DIyaL4YE0Htk533NZkexQRERFpLRR0RH7H3qK93LL0Fm5bdhv7i/cTbAvm4TMe5rOLPyM8O56S3Eq8fD3pVvE2uKrJSU/AbHewMziG4ddfhtXj2N9mMSE+RN1/H7negXgWFHPoYG8wXDBvKrgcYK+AlS9QkOYHbhOHYzoTd84Zzbh7ERERkVOTR0sXIHKyKqoq4vUtr/Np2qe4DBceZg+u6XoNN/W+iQBrAIZhsHnJQQB6DrDhuf1DHOUWCrdUYAaWDZnAzD7Rv/s5E87qwhMX38SET5+lbEMu5WFt8GUrrH4JLFZchbkU7KlpPBB8/Q1NuGMRERGR1kNBR+T/cbgcfJz2MW9seYNSeykAw2OGc0//e2gf0L52XOauInIOlmLxNNOz+g0w3GSl98DsKmBzWAKXXDcWi9n0u59nMpm45b5r+GLjD5y9bx171obTc3g+5u+fAU8fCvf6gtNEVnBbzrl8dJPtW0RERKQ1UdAR+ZlhGPxw6Aee2/gcB0oOANA5uDP3DbiPQW0HHTW2osTOD5/sAqBbTxPe+7+kusRK6U+FmIA1517Oy4kRx/3ZoX42Okx/iPybJ9GmoJhDB3vQvtM23HY7eWltAXBdMQmzpW5TAxERERGpS0FHhJqQ8+S6J/k47WMAQrxCuD3pdsbFj8NiPjpclBdVM/elzRRmVeATYKWv8yUAjhzohsnI5ce2Pbjq2lGYTL9/mvNbFwxKYOaEm7ngo2cp3VhIRVgA1blOjCoT+T5BnHnL1Y2yVxEREZHTgZoRiAD/2f4fPk77GBMmpvSYwtfjvuayzpfVCTmlBVV89XwyhVkV+AXbGDe+DP+cb6ks9qVyRy5uTGwdeRVD4kP/UB1T7v0TazsOwGwY7Fjfgb27au7xyR05Hi9vrxPep4iIiMjpQkFHTnuLDyzmxU0vAvD3gX/n7n5342f1qzOuOLeCr55Lpji3koBQL8bd1ZugTY8AkLmn5oGg38X0Zco1w/9wLQFennR74hEKbP745uXiW1xGmac3Q+68/g+vKSIiInI6UtCR01pKTgoPrnwQgGu6XcM13a6pd1xhVjlfPZdMaUEVQRE+jLunLwGH50JuKuWFIdh35+AwWUi/5Gp6xwSdUE1nJHVk55W31P569+ALiYgMOaE1RURERE43Cjpy2sooyeCO7+7A7rZzbsy53Nv/3nrH5R8u46sXNlNebCeknS+X3p2EnzkfljyEYcDhnTEALOowiJuuOKtRarvmb9fyfc/z2BUcQ9JdNzfKmiIiIiKnEzUjkNNSUVURty27jcLqQhLbJPL0WU/XuR8HIDe9lHkzU6gqdxAa48cld/bB28cC798IlYWUVXbDdTCXKosnJRMmkxDh3yj1eXla+NOHL1Fc6aBdkHejrCkiIiJyOlHQkdOO3WXnzuV3cqDkAG192/Lq8Ffx8fSpMy5rXzHzX9mCvdJJeFwAF9/eGy9fT1jxFBxcjeHpR/pmf8wUsyD+LG4eN6BR6/S1eeBr07eoiIiIyB+hS9fktGIYBg+vfpjknGT8PP147bzXCPMJqzMuc3ch82amYK900jY+kLF39qkJOQdWw/dPA1Ac+GfMGYco9fTGdOWfiAmpG5ZEREREpGXox8VyWnkt5TW+2f8NHiYPXjj3BRKCE+qMyUgt4JvXf8LpcBPdNZjRt/bC02aBigL48kYw3Dhix3PojWVYgHldhnHHmD7NvhcREREROTYFHTltzNkzh7d+eguAaYOnMbjd4DpjDmzNY9Fb23A53cT2aMOFN/XAw2oBw4C5U6HkMO6gTuyfX4KlpJi9ge0ImjyZcH8940ZERETkZKKgI6eFtUfW8uiPjwJwY88bGZcw7qj33S4321dmsuqz3bhdBh37hHHB9d2xeP58def6dyDta7BYycw8E9eObyn19OaDUbfyrwsSm3s7IiIiIvI7FHSk1dtbtJe7l9+N03AyKm4UU5Om1r5nGAb7t+Sxds5eCrMqAEjoH855UxKxWH4OOVlbYck/ACjyvZrSj5YA8NaQP/Hs7aPx9/Js3g2JiIiIyO9S0JFWLa8yj9uW3kapo5Sk8CQeO/MxzKaaAJO1r5gfv9zDkT3FAHj5eTLgojh6nBON2WyqWcBeDp9NAVc1VYHncujdFViAT7qezy0PXEv7NmpAICIiInIyUtCRVqvSWcnty24nszyT9v7tmTlsJjaLjaLsCtbO3cve5FwAPDzN9B4RQ98LYrF6/79viYX3Qf5uXLa2pH1VgtVhZ1N4Z3o/fA8D4kJaYFciIiIicjwUdKRV2pa3jWk/TmN34W6CbEG8PuJ1bHZffpizi+0/HMbtNjCZoOuQtgwc0xG/YFvdRbZ+Dps/xDBMpP3UE2vONrK9gyj46z+Y1C+2+TclIiIiIsdNQUdalUpnJa+nvM6sHbNwG26CbEG8dObL5K4yWLxkDY4qFwCxPdsw+NJOtInyq3+hgv0w/68AHC4biWnzTzjMFr6/+m4evrR/M+1GRERERP4oBR1pNdYfWc8jax4hozQDgNGxF3EZ17P1lWzKiwsBCGvvz5AJ8UR3CT72Qk47fH4d2Espcfeh+JutmIEFZ1/JfXeO//X+HRERERE5aSnoyCmvxF7CCxtf4IvdXwAQZ+rMJKZSsdCT9cU1oScg1IszxnYivl84pt8LKt89BpnJ2F3B7P66Ah/DYHWngVz/9D14Wy1NvR0RERERaQQKOnJKW56+nMfXPk5eeT6dCpI4t+xSPI8EkYcB2PH296TvyFh6nhP96zNx/pc9S+HHlzFcsGV1B/wqsjgQ2I4hrzxLRKB3k+9HRERERBqHgo6ckvIr83lq/VOsTU2mW/YQxuQNwur4udWzCWK6hZA4tB0deodi8TiOgANQmgVf3QJA6t7++GVmUubphdeTz5DYMbyJdiIiIiIiTUFBp4GcyXNxHtyL17i7W7qU05JhGMxLW8BnixYRd7gPV5aOqn3PN9BK58FtsXUOYH9lNbMz8ziSmkGnMD96RAXSMzqQdoFemEz/79K1gn2w7m3Y/CHYS8nO7YgpOROAjJvuZfx5/ZpziyIiIiLSCBR0GqDym3fJuP9pzFboOPRSzOEdW7qk08r2g2l8/ukK/A9EMdg1oeZFE5ijvMkM8WCTvZLdG9JwrDWOuUYbX2tN6GkXwNmeqfQ8PBuvfd9iomZOqdGJIyuc2ICt51zK5VOvaoadiYiIiEhjMxmGcey/FZ4kSkpKCAwMpLi4mICAgBarw11ayt7hg3GWumhzdhThby9tsVpOF4ZhsCFrA3O+WU5ocndsrprL00o9K0mxmNlqdVP+/65MC/DyoFvbABLbBRAV5M3u7DJ+OlzM7uxSLO5qxllW8WfLYrqaM2rnbPEawN6wcQS/9SmRJTnsa5/I+Qs+xmr1bM7tioiIiMjvON5soBOdBqi2+bBx1JX0+fQj8lceInD5f7EN00/8m4LT7WTpwaV8tOkTIjb1oUPhAACyvHNYZTPYbwSAyU1MiDdntg0gsW0g3dr614abOpenFR/Gue5t3Ovew51fhr3UgyOlQewpaUdxiQ9tyoroZn8VgELfYAb/53WFHBEREZFTmIJOA2zPLOZBexKzor4m9HARR/75BLFDx2GyerV0aa1GhaOCr/Z8xQc7PsDrQDhn7b8cL6cvLlys9i2lMjaKa/tFk9gukK5t/Qnwqj+MGA4HlVu3UfnDfOyblmFPz8BeasFZ4QP41I4LpYhQimp/XRgSSeQzTxMSFdHEOxURERGRpqSg0wC9IgK4yzuYBV3u58/Zf6fyiIui5+4k+MG3Wrq0U15eZR6zU2fzSdon2MtdnLX/cjrlJwGQbXGw2NfNVRf04C/D4vG01O2iZjgcVG7bRsXqH6hYuYyK1H0YdtdvRthq/80cEIC1QxzW2FiscXHY4uLwjI3FGhuHxc+3qbcqIiIiIs1AQacBCo6U45FdTXu3PyuTruasDf8l5+Pv8b9yGx4de7R0eaekfcX7mLV9FvP2zsPhdhCX34thB67EZvfFhcFam5OcGCvvXJlEr+ig2nm1wWbdOipWLqViWxpGtfOotS1WF97hLmwJCVgHXoS1z5lY4+KwBAXVvbRNRERERFoVBZ0GiOwYyIBxMaz/Ip1q36HkR2+izaFdZN9/K1GfrmzWWvIr80nOSWZb3jbOjDqTAZEDmvXzG8PC/Qv5+w9/x8DA6vRmQuYthB2OByDX7OYbHzsXnRPLuxd2xcvTgjMvj6Ivv6Ji9QoqUrZiVDuOWs9ideETbsenYzA+ZwzGNvRSTB3OBKtPfR8vIiIiIq2Ygk4DVDgqmGl/hMTY8wk8GMPG+Fs5J+9R+CmPoC/ewHfCrU322ZllmWzK3lT7daDkQO17n6R9wpyxc4j0jWyyz29spfZSnlr/FAYGF5onkJB6Do4yAzcG621O9kV48OIVAxkaH4pht5P/7/fIe/Vl3JX22jVqg02UBZ/+/bENvghTwnAIjG7BnYmIiIjIyUBBpwF2FuwktSCV7RE7uLroQXyKQ0jueyuD1j7LkWdeoePIazD7nXj7a8Mw2F+8n43ZG0nOSWZT9iayyrOOGmPCREJwAtWuag6WHOSRNY/wxnlvnDKXZL255U1KyssYc/hGog/3wIFBvtnNQh87gwe049VLuhPo7UnZypVkP/ow9kPZAHgF2wnsWI1Pry7YBo7E1Pk8aNsHzJaW3ZCIiIiInFT0HJ0GWn9kPbd/dzuWMi+u2PZ3PB1eROWspMuOj2kzpi/hz330h9fOq8zjhY0vsOrwKgqrC496z8PkQWKbRPpF9KNfRD/6hPch0BbIvuJ9XD7vcuxuO48PfZyx8WNPdItNbn/xfsbPGc/wtMl0LOiNgcEmm4utIfDY+J6M6tkWe3o62Y8+RNnqjQBYbC7C+7sJvOFeTElXgVfL/n8gIiIiIi3jeLOBgs4fsC1vG7cuvRW/7EguSr0FEya6pH1EVPZqOs5+F1ufIQ1ec0PWBu774T7yKvMAsFls9A7rTd+IvvSL6Eev0F74eNZ/r8m/t/6bl5Jfwt/qz9yxcwnzCTuh/f2iyuHi36v2M6xLOIntGu/3/balt1GwEc46cBkuDL7wtRPfM5QZE3oSanaR98qLFHwwG8NlgMkgpHMloVMmYhn5IHgHNVodIiIiInLqUdBpYnuL9nLTkpuI2tOLQekXY3I76bv5RcKC8ui4eB0mc90WyPVxG27e3fYur2x+BbfhplNgJx464yH6hPXB03J8D6x0up1c88017MjfwbCYYcwcNrNRLmF7Y8Ve3vgmDYfVxMtX9+X8xBN/tswPh35g+twZjNt2FxbDg+99HIy7shsT+0dTOvcrcp5+EmdRBQC+kVVEXN4f21VPQ0jHE/5sERERETn1HW82OL6/jUsdnYI68f6o98nrksa+kC0YZg+29biBsiwT+W88eVxrFFUV8Zdlf2Fm8kzchpuLO1zMi93eok1WHNm7S8k+UEJhVjllhdXYK50Y7vozqYfZg8eGPoaH2YPlGctZfGDxCe/PMAx+/D6dm0u8GFPkwW2zNjJrzYETWtPhcvDc6pc4f9cULIYH+6xubrs5iUu9i0kfdyGZD/wDZ1EFnr5Ooi8JJOa9j7D95TOFHBERERFpMJ3onKDcilxuWziV3qvGElwZSVDRLpJ2vkLnJcvwCD92F7SUnBTu/eFessqzsFls3Bv3MNb1MWTuKvqfn+dps+DpZcHq5YGnzYLV20Li0HZ0HhjJGylv8PqW1wm2BTPn0jmEeIX84X1t2l/AohdWE+SoeYDmbk8nc30c3HROR/5+YVfM5oafGL226d/s/bKITvlJlFrcDLuuI+0/fIaihd8DYLK4Ce1rJuTOf2DuexUc56mYiIiIiJw+dKLTTMJ8wvjXmLc5cMZq7OYqioI6syfqUvbceV294w3DYNb2WUxZNIWs8iw6W7vzYMWr5H3kT+auIiyeZiI6BBDc1he/YBs2Hw9MvwkVjmoXFcV2irIryE0v5XBaEcs/2ElpQRU39LyBzsGdKawuZMa6GX94T4Zh8N6XnxLk8MVFOS6TgwSHBxdWePLW9/u44+PNVDlcDVpze/ZhVn6bQqf8JNy4OWd0G0LvvrI25ATE2en05FWE/nsT5v7XKOSIiIiIyAnRiU4jqXBUMO3DZ+mw7iwAEne8S+f7JhA1ZlztmOLqYqatnsZ3Gd/h4bIyoeImQtO64HK4Aeg8MIIzLu2Ef4jXUWsbhoHL4cZe5cJe5cRR5cJR7cRe5WLjNwfI3l9Cl0GRjJiSyPb87Vzz9TW4DBcvnfsS58We16B9GIbBS+texfFRFL6OIBJ2f4anPZ/t3W/EhIXNXk6W2hwM7BDC25P7EeRj/d019+SUcfMH/+SSPefhYXjSuQfEvnsnrgonHt4uoq4dgM/1L4D/id8DJCIiIiKtm050mpmPpw9PTn6A/G67ANjZZRJ7nn0DR2UVANvztjNxwUSWpy8nMXcIN25/iuBtCbgcbgJj/PAdE8U3PnYmv7qIP7+1irkph2tPTUwmEx5WCz4BVoLCfQhr70+7hGDieoZy9pWdAUhbl0XOwRK6t+nOlB5TAHh83eMUVxcf9x4Mw2Bm8kw2Ld+NryMIz+oCojJXEZm7le6pHwCQVOXBMKeV9QcKGP/Gj2QUVPzPNbcdLubqf33O8Iy+eBieBAQUEv3GVFwVTrxCHMS9cA8+f/1IIUdEREREGpVOdBqZ3eng5emf4J3fDq/KXLy8PsFy1xU8s+EZwgs6cHbG5QSWhQPg8HCRXbaLgJwUOhZn0qHkCP6OSoqtPszvMJTliecwfFBnrugfQ8+owGN2Ulv63g7S1mbRNj6Qcff0xe62c/n8y9lfvJ9LOl3CE2c+8bt1G4bBC5teYPaWj7l68zS8nL503fkBMRVbCLl0BDkfLOBw27PY1XkiABuDDZYbVYT6WXn3zwPoFR1UZ831+wu4/j/rGW7fR+eirpgo4cxVj+PpLMcvxknUzFcxJ47847/ZIiIiInLaUXvpFlRZZufd++aBO4Tggu0sTJxL58JLCKnuAYDZWUncwYW0P/Q9ZsN5zHWqLZ4sbj+AL+PPIbhTHJf1i2ZcUhRt/GxHjSsrrOKjaWtxOtxceFMPOvUNJyUnhckLJ2Ng8Np5r3F29NnH/BzDMHhmwzN8mPoh/dNH0f/whXhU5XHmukeJ6FFEaPcySi1D2D33MNkhw9nX8RIAtrS1sKSyDG9PC69encR53X49lVm+M4dbPtxEN9dhzi/sCIaLfptfJLBkPyG9TYTP/AxTZPcT+W0WERERkdOQgk4Ly951iC+e24Zh/vUeFpPbRVTm93Q4sAhPZzlmqxuvIAdewQ5sQQ68ghxY/Z2UZXqRn+pHVWHNXDcmfojqzecJw0hvE815XSO4YkA0ZyeE4WGpufpw3fx9bPz6AAGhXlw9/Qwsnmae3fAss3bMIsIngq/GfoW/1b9OnYZh8NT6p5i9czZeDj8mb34Ms8tMj23vEFGwmfhLsvHwNoHhoqzaxuoNEVTbLiYjZgRgsLuTjTn5xZhN8M+xPZh0RizztmRy9ycpBDsc/KnMC4vhSae9c4g9tITI8/wJnjEf/I/dkU5ERERE5FgUdE4CKW9/wOrkKADCCzfTs+RTQv0OYQty4hUTgkdULKaQOAiKheDYmn/6RcCOORhr36RifzH5qX6UZ/3anCA5rDOfJZxLSlgC4QFejO8bzXVnxhHk6cFH09dSUWxnyIR4ks5vT6WzkgnzJpBRmsFlnS9j+uDpR9XnNtw8ue5JPkn7BBMm7qp+iopkL6jKZdjaRwjqUEG7K3vDRc/D4gdh9xLyDTOf7Y4kvPxqjrQdislwkZ9o5d9HygAY0S2cZTtz8HDDLZVOvOz+tMnfTp+drxEzsSO+934KNr9m+28gIiIiIq1Lkwad1157jWeffZasrCx69+7NK6+8wsCBA485/rPPPuPhhx/mwIEDJCQk8PTTTzN69Ojj/rxTNehgGKR/+i4e9jzadQv/NcwERoOH7X/PdVTBltnw4ytU7U0nf6cfJeneYNTcp7M/OJpPOp3Dyna96BQZyMI7z2L3umy+m5WK1duDSf88A29/KxuyNnDd4ppW1+9c8A5ntD0DqAk5j619jM93fY4JEw/3+Ce57wZiuAx6/vQqYQWpdBiZg9d9SyCqX01Nu5bA4gc5ULyfmUURDDpwHfmhfTG77VgT3TyW9fP/Sgbc5bLjURaItbqIvqmP0+PG/tj+/DpYPJrkt1pERERETg9N1nXtk08+4e6772b69OkkJyfTu3dvRo4cSU5OTr3jf/zxR6666iquv/56Nm/ezKWXXsqll17Ktm3bGvrRpx6TifYTr6fdn/4O/adAp+HQptPvhxwATy/ofx1M3YjXze8SNaEjnS7KIbhzGSaLmw6Fh7h/40f8Z9kzBP20no83ZND1jEhCY/ywVzrZsGA/AAMiBzCxS00DgUd+fIQKRwVuw82jax6tDTmPn/k4bbZ3wXAZ2KtzCC1IxSesGq+hF/0acgA6XwC3/kjcef9kUttKZg+chU/pDtxmK85tbl73yaJdgI3HjDw8ygLBcBOe/W96Tb8C23VvKeSIiIiISLNp8InOoEGDGDBgAK+++ioAbrebmJgYbr/9du6///464ydOnEh5eTkLFiyofe2MM86gT58+vPnmm8f1mafsiU5jMgw4sApWz8S5fSmFu30p3O2Lq9pCtcWDey95mK8eGU/xgVLmvLgZk9nElQ8PJKStL+WOcsbPHU9meSYTu0ykylnF3L1zMZvMPHHmEwz1GcZ/H12HYUDiTzOJLNhF1JlFBDy1EkLj66+nLJeFi27nsdxd/GnDrbitnbDaS+hR8T0p/iNxW6xYyuZx7q2d6Dr09ub9vRIRERGRVqtJTnTsdjubNm1ixIgRvy5gNjNixAjWrFlT75w1a9YcNR5g5MiRxxwPUF1dTUlJyVFfpz2TCTqcBZM+x+POVYRdPYb4sfl4h9qxuZz8ad1/efuHfUR1CaZD71AMt8GPX+wBwNfTl+lDau7P+STtE+bunYvFZOGps55iTMcxrJu3D8OAYkcukQW78PR14n/x5ccOOQB+YYy67GOu6z2efw15GzsZ2K0BJAddjNtixe5OJfdPDoUcEREREWkRDQo6eXl5uFwuIiKOfrhjREQEWVlZ9c7Jyspq0HiAGTNmEBgYWPsVExPTkDJbv8geMP4tzHenEHlhJJgMBmTuZNOnC8gpqWLI+HjMZhMHt+WTviMfgCHthjA+YTwAFpOFp89+mlEdRpFzsIS9ybkYQP/dswEI7mLHNKzu6Vx9rh/8EJckjuG//d6i0lJz+aLDVMy8IV/yl3Mea/y9i4iIiIgchwbfo9McHnjgAYqLi2u/MjIyWrqkk1NQDF63fUBwVwcAd23+gJkLtxEU4UPPc6MBWP35HtzumqsT7xtwHzf2vJG3zn+LkXE1D+pcO3cfANnuAmJydmHycBM08WoIaHdcJZhMJh464yH6dujF571fJaXdMr7q+QbXD5xCiFdIY+9YREREROS4NCjohIaGYrFYyM7OPur17OxsIiPrfy5KZGRkg8YD2Gw2AgICjvqSYwjpSNj9j+Lh7cK3rIroj19gd3Yp/S+Kw+bjQUFmOamrM4GaS9ju6HsHg9oOAuBwWiEZOwrABAP3fwhAULwLywX3NagED7MHz5/zPNGRkayNnUdQO28mdp3YuPsUEREREWmABgUdq9VKv379WLZsWe1rbrebZcuWMXjw4HrnDB48+KjxAN9+++0xx0vDWQZNImJcLwDOTUtm1gfz8fL1ZMCYDgCsm7cPe6XzqDmGYbB27l4Asr0riM/YCUDwpEngHdzgGnw9fXljxBtMTpzMs+c8i6fZ80S2JCIiIiJyQhp86drdd9/NO++8w/vvv09qaiq33nor5eXlTJkyBYDJkyfzwAMP1I6/8847WbRoEc8//zw7d+7kkUceYePGjUydOrXxdiH4//09vKPN4DYxfsHTrNmdS49zogiK8KGy1MGmxQePGn9gaz5Z+0owe5jos+ffgAnfaAPbJQ07zfmtMJ8w7h1wL52DO5/gbkRERERETkyDg87EiRN57rnnmDZtGn369CElJYVFixbVNhxIT0/nyJEjteOHDBnC7Nmzefvtt+nduzeff/45c+bMoUePHo23C8Fk86PtjBfBbOCVZefIS3/FZDIxZHwnALYszaAkvxIAw22w7ufTnOq2kLgvFYCQa64Aq0/LbEBEREREpBE1+Dk6LUHP0Tl+h+65ltKv12PxdnFkxvOcPXIMc1/azOG0IhIGRHDB9d1JW5fF0v/swOptITz7JTon78QjyET8qi2YPHTJmYiIiIicvJrkOTpy8mv3+JsQ4Imr0kLUm/diryhi6GUJYILdG7LJ3F3E+vk1ndYCE20k7toOQMhlFyvkiIiIiEiroaDTypi9vQmbXvP8GkcapL84hbBoP7oNbgvA169toSSvCu8AKxFbn8VZZgGrieBbp7Vk2SIiIiIijUpBpxUKvWgslT27g2HCuvgnytfNYtAlHfGwWbBXuQBIHOxN7JbNAHiefy5mX9+WLFlEREREpFEp6LRSiS+8hMvDTEWujaq3/oGv4yB9L2gPgH8bL4K3PUlltg3DBLF3P9TC1YqIiIiINC4FnVbKKyaaiqtuACA/2ZvK9/9E3/MiOPOKBC66wgf/NRsBKO/VC8+oqJYsVURERESk0SnotGL9/3Yb+SERuKotFK84gmXZNHoPj8Fj1TRKDnoD0P72u1q4ShERERGRxqeg04qZbTaCHqxpMlC4x4fKxe/Bkn/g+H4ThstEQXg7wocOatkiRURERESagIJOK9djzHB29RwKmMjaGIh75SsU7q5pPGCaOBmTydSyBYqIiIiINAEFndNAnyenUe7hRVWBlUOrQnBWWij18qX/dRNbujQRERERkSahoHMaiE1oz56LJwFQfsQLgIyzRmP19mrJskREREREmoyCzmniwgf/wv7gaAAcJguJt0xp4YpERERERJqOgs5pIsjfi+K//I1iqy+r+oygW/cOLV2SiIiIiEiT8WjpAqT5TLz6AuZ2786Y9iEtXYqIiIiISJNS0DmNmM0mxiVFt3QZIiIiIiJNTpeuiYiIiIhIq6OgIyIiIiIirY6CjoiIiIiItDoKOiIiIiIi0uoo6IiIiIiISKujoCMiIiIiIq2Ogo6IiIiIiLQ6CjoiIiIiItLqKOiIiIiIiEiro6AjIiIiIiKtjoKOiIiIiIi0Ogo6IiIiIiLS6ijoiIiIiIhIq6OgIyIiIiIirY6CjoiIiIiItDoKOiIiIiIi0uoo6IiIiIiISKvj0dIFHA/DMAAoKSlp4UpERERERKQl/ZIJfskIx3JKBJ3S0lIAYmJiWrgSERERERE5GZSWlhIYGHjM903G70Whk4Db7SYzMxN/f39MJlNLlyMiIiIiIi3EMAxKS0tp164dZvOx78Q5JYKOiIiIiIhIQ6gZgYiIiIiItDoKOiIiIiIi0uoo6IiIiIiISKujoCMiIiIiIq2Ogo6IiIiIiLQ6CjoiIiIiItLqKOiIiMgpxzAMnE5nS5chIiInMQUdERE5KbjdbmbMmEGHDh3w9vamd+/efP755wCsWLECk8nEwoUL6devHzabjVWrVrF3717Gjh1LREQEfn5+DBgwgKVLl7bwTkRE5GTg0dIFiIiIAMyYMYMPP/yQN998k4SEBH744QcmTZpEWFhY7Zj777+f5557jo4dOxIcHExGRgajR4/miSeewGazMWvWLC6++GLS0tJo3759C+5GRERamskwDKOlixARkdNbdXU1ISEhLF26lMGDB9e+fsMNN1BRUcFNN93EsGHDmDNnDmPHjv2fa/Xo0YNbbrmFqVOnNnXZIiJyEtOJjoiItLg9e/ZQUVHB+eeff9TrdrudpKSk2l/379//qPfLysp45JFH+Prrrzly5AhOp5PKykrS09ObpW4RETl5KeiIiEiLKysrA+Drr78mKirqqPdsNht79+4FwNfX96j3/va3v/Htt9/y3HPPER8fj7e3N5dddhl2u715ChcRkZOWgo6IiLS4xMREbDYb6enpnHPOOXXe/yXo/H+rV6/mz3/+M+PGjQNqAtOBAweaslQRETlFKOiIiEiL8/f3529/+xt33XUXbrebM888k+LiYlavXk1AQACxsbH1zktISODLL7/k4osvxmQy8fDDD+N2u5u5ehERORkp6IiIyEnhscceIywsjBkzZrBv3z6CgoLo27cvDz744DHDywsvvMB1113HkCFDCA0N5e9//zslJSXNXLmIiJyM1HVNRERERERaHT0wVEREREREWh0FHRERERERaXUUdEREREREpNVR0BERERERkVZHQUdERERERFodBR0REREREWl1FHRERERERKTVUdAREREREZFWR0FHRERERERaHQUdERERERFpdRR0RERERESk1fk/bJ7orFPJKuIAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from numerai_tools.scoring import correlation_contribution\n",
        "\n",
        "# Download and join in the meta_model for the validation eras\n",
        "napi.download_dataset(f\"v4.3/meta_model.parquet\", round_num=842)\n",
        "validation[\"meta_model\"] = pd.read_parquet(\n",
        "    f\"v4.3/meta_model.parquet\"\n",
        ")[\"numerai_meta_model\"]\n",
        "\n",
        "def get_mmc(validation, meta_model_col):\n",
        "    # Compute the per-era mmc between our predictions, the meta model, and the target values\n",
        "    per_era_mmc = validation.dropna().groupby(\"era\").apply(\n",
        "        lambda x: correlation_contribution(\n",
        "            x[prediction_cols], x[meta_model_col], x[\"target\"]\n",
        "        )\n",
        "    )\n",
        "\n",
        "    cumsum_mmc = per_era_mmc.cumsum()\n",
        "\n",
        "    # compute summary metrics\n",
        "    summary_metrics = get_summary_metrics(per_era_mmc, cumsum_mmc)\n",
        "    summary = pd.DataFrame(summary_metrics)\n",
        "\n",
        "    return per_era_mmc, cumsum_mmc, summary\n",
        "\n",
        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"meta_model\")\n",
        "# plot the cumsum mmc performance\n",
        "cumsum_mmc.plot(\n",
        "  title=\"Cumulative MMC of Neutralized Predictions\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bijsOnqqOqpf"
      },
      "source": [
        "#### Benchmark Models\n",
        "\n",
        "It's no accident that a model trained on `teager` nicely ensembles with `cyrus`. We have seen in our research that models trained or ensembled using `teager` perform well. We even released a benchmark for a [teager ensemble](https://numer.ai/v42_teager_ensemble). We submit predictions for all internally known models [here](https://numer.ai/~benchmark_models) and release files with their predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "QNSLiLDWOx6D",
        "outputId": "18224a74-f6de-4bc8-cab1-84d7552b1873"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v5.1/validation_benchmark_models.parquet: 144MB [00:02, 58.2MB/s]                           \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "benchmark_models"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-5b565ee8-cfd2-4024-aa8e-474663c69c93\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>era</th>\n",
              "      <th>v5_lgbm_cyrusd20</th>\n",
              "      <th>v5_lgbm_teager2b20</th>\n",
              "      <th>v5_lgbm_ct_blend</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n000101811a8a843</th>\n",
              "      <td>0575</td>\n",
              "      <td>0.323319</td>\n",
              "      <td>0.506974</td>\n",
              "      <td>0.404506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n001e1318d5072ac</th>\n",
              "      <td>0575</td>\n",
              "      <td>0.860873</td>\n",
              "      <td>0.823677</td>\n",
              "      <td>0.851931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002a9c5ab785cbb</th>\n",
              "      <td>0575</td>\n",
              "      <td>0.749285</td>\n",
              "      <td>0.950823</td>\n",
              "      <td>0.883941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002ccf6d0e8c5ad</th>\n",
              "      <td>0575</td>\n",
              "      <td>0.981402</td>\n",
              "      <td>0.977289</td>\n",
              "      <td>0.981760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0041544c345c91d</th>\n",
              "      <td>0575</td>\n",
              "      <td>0.862482</td>\n",
              "      <td>0.831903</td>\n",
              "      <td>0.857117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffaa77add7e2a53</th>\n",
              "      <td>1175</td>\n",
              "      <td>0.085748</td>\n",
              "      <td>0.071036</td>\n",
              "      <td>0.070590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffd21984a44c53f</th>\n",
              "      <td>1175</td>\n",
              "      <td>0.350572</td>\n",
              "      <td>0.440184</td>\n",
              "      <td>0.392183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffd33ef0b6cd58e</th>\n",
              "      <td>1175</td>\n",
              "      <td>0.913211</td>\n",
              "      <td>0.987517</td>\n",
              "      <td>0.969089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffe015b219dd580</th>\n",
              "      <td>1175</td>\n",
              "      <td>0.413583</td>\n",
              "      <td>0.331253</td>\n",
              "      <td>0.368405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nfff7094a2835336</th>\n",
              "      <td>1175</td>\n",
              "      <td>0.308813</td>\n",
              "      <td>0.147867</td>\n",
              "      <td>0.210284</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3720004 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b565ee8-cfd2-4024-aa8e-474663c69c93')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5b565ee8-cfd2-4024-aa8e-474663c69c93 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5b565ee8-cfd2-4024-aa8e-474663c69c93');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-0efdd40e-2dd4-45d5-97e9-0271f2b9d40c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0efdd40e-2dd4-45d5-97e9-0271f2b9d40c')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-0efdd40e-2dd4-45d5-97e9-0271f2b9d40c button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_bacd99dd-9f1a-47c0-bc9d-ddb3d454f94f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('benchmark_models')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bacd99dd-9f1a-47c0-bc9d-ddb3d454f94f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('benchmark_models');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   era  v5_lgbm_cyrusd20  v5_lgbm_teager2b20  v5_lgbm_ct_blend\n",
              "id                                                                            \n",
              "n000101811a8a843  0575          0.323319            0.506974          0.404506\n",
              "n001e1318d5072ac  0575          0.860873            0.823677          0.851931\n",
              "n002a9c5ab785cbb  0575          0.749285            0.950823          0.883941\n",
              "n002ccf6d0e8c5ad  0575          0.981402            0.977289          0.981760\n",
              "n0041544c345c91d  0575          0.862482            0.831903          0.857117\n",
              "...                ...               ...                 ...               ...\n",
              "nffaa77add7e2a53  1175          0.085748            0.071036          0.070590\n",
              "nffd21984a44c53f  1175          0.350572            0.440184          0.392183\n",
              "nffd33ef0b6cd58e  1175          0.913211            0.987517          0.969089\n",
              "nffe015b219dd580  1175          0.413583            0.331253          0.368405\n",
              "nfff7094a2835336  1175          0.308813            0.147867          0.210284\n",
              "\n",
              "[3720004 rows x 4 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# download Numerai's benchmark models\n",
        "napi.download_dataset(f\"{DATA_VERSION}/validation_benchmark_models.parquet\")\n",
        "benchmark_models = pd.read_parquet(\n",
        "    f\"{DATA_VERSION}/validation_benchmark_models.parquet\"\n",
        ")\n",
        "benchmark_models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZNr_xbQWJsf"
      },
      "source": [
        "Because models trained on newer targets perform so well and we release their predictions, it's likely many users will begin to shift their models to include newer data and targets. By extension, the Meta Model will begin to include information from from these new targets.\n",
        "\n",
        "This means that MMC over the validation period may not be truly indicative of out-of-sample performance. The Meta Model over the early validation period did not have access to newer data/targets and MMC over the validation period may be misleading.\n",
        "\n",
        "So if the Meta Model was much closer to our teager ensemble, what would your MMC look like?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "OcUNnnkUWnwg",
        "outputId": "65de24b0-9515-4205-ede5-8d5649fadc23"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipython-input-18-2344724651.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005840424856666204,\n        \"min\": 0.0007689598505097739,\n        \"max\": 0.002248971292499629,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0007689598505097739,\n          0.001728447785690628,\n          0.001022914220732764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0009741581656185877,\n        \"min\": 0.015378548779248912,\n        \"max\": 0.01762771845091716,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01762771845091716,\n          0.01623383171438237,\n          0.015378548779248912\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03386493463644543,\n        \"min\": 0.04362219947243175,\n        \"max\": 0.13064866701232172,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.04362219947243175,\n          0.10647195413263456,\n          0.06651565342192992\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019273897831462303,\n        \"min\": 0.1363086457209609,\n        \"max\": 0.18223874478023522,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.18223874478023522,\n          0.1411517070138636,\n          0.14270543918631223\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "summary"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <td>0.002249</td>\n",
              "      <td>0.017214</td>\n",
              "      <td>0.130649</td>\n",
              "      <td>0.136309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <td>0.000769</td>\n",
              "      <td>0.017628</td>\n",
              "      <td>0.043622</td>\n",
              "      <td>0.182239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <td>0.001023</td>\n",
              "      <td>0.015379</td>\n",
              "      <td>0.066516</td>\n",
              "      <td>0.142705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <td>0.001499</td>\n",
              "      <td>0.017574</td>\n",
              "      <td>0.085287</td>\n",
              "      <td>0.163304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_teager</th>\n",
              "      <td>0.001728</td>\n",
              "      <td>0.016234</td>\n",
              "      <td>0.106472</td>\n",
              "      <td>0.141152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8a87ccf2-1aa5-4efc-9f0b-487197b8e130');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-bc3e8048-9a50-4cd7-97d8-04cfeeae0a14\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc3e8048-9a50-4cd7-97d8-04cfeeae0a14')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-bc3e8048-9a50-4cd7-97d8-04cfeeae0a14 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_b3fc122e-9528-4542-afd3-4094c0927687\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b3fc122e-9528-4542-afd3-4094c0927687 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20   0.002249 0.017214 0.130649      0.136309\n",
              "prediction_target_victor_20   0.000769 0.017628 0.043622      0.182239\n",
              "prediction_target_teager2b_20 0.001023 0.015379 0.066516      0.142705\n",
              "ensemble_cyrus_victor         0.001499 0.017574 0.085287      0.163304\n",
              "ensemble_cyrus_teager         0.001728 0.016234 0.106472      0.141152"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "validation[FAVORITE_MODEL] = benchmark_models[FAVORITE_MODEL]\n",
        "\n",
        "\n",
        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, FAVORITE_MODEL)\n",
        "# plot the cumsum mmc performance\n",
        "cumsum_mmc.plot(\n",
        "  title=\"Contribution of Neutralized Predictions to Numerai's Teager Ensemble\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KSqVvJBxnNh"
      },
      "source": [
        "Ouch. Our teager models actually perform the worst. This means we aren't adding very useful signal to a model that Numerai already created, but this should not be surprising since we are training basically the same model. The model trained with `xerxes`, however, still does well against Numerai's model. What do you think this means?\n",
        "\n",
        "It's also helpful to if we measured the contribution of your models to all of Numerai's benchmark models. We call this Benchmark Model Contribution or `BMC`.  On the website, `BMC` measures your model's contribution to a weighted ensemble of all of our Benchmark Models.\n",
        "\n",
        "This is an important metric to track because it tells you how additive your model is to Numerai's known models and, by extension, how additive you might be to the Meta Model in the future.\n",
        "\n",
        "To keep things simple, we will use an unweighted ensemble of Numerai's Benchmarks to measure your models' BMC, let's take a look:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "39UfnEmifTMh",
        "outputId": "827dd9fb-4682-418c-eecd-f3d1e5c90114"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipython-input-18-2344724651.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  per_era_mmc = validation.dropna().groupby(\"era\").apply(\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"summary\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005791811664620577,\n        \"min\": 0.0008034731248098968,\n        \"max\": 0.0022759138927917312,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0008034731248098968,\n          0.0017622507691957077,\n          0.0010691338428035462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0009823886659102547,\n        \"min\": 0.015359786624393811,\n        \"max\": 0.01762276674321235,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01762276674321235,\n          0.016227870265374658,\n          0.015359786624393811\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sharpe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03357080235163665,\n        \"min\": 0.045592904707733556,\n        \"max\": 0.13213656698801804,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.045592904707733556,\n          0.10859408784871882,\n          0.06960603483290514\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_drawdown\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019204231203145364,\n        \"min\": 0.13583481055610255,\n        \"max\": 0.18156932774894002,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.18156932774894002,\n          0.1405128471968606,\n          0.1422866260231198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "summary"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-29f7286c-8964-4335-b3cd-7eab7651951a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sharpe</th>\n",
              "      <th>max_drawdown</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>prediction_target_cyrusd_20</th>\n",
              "      <td>0.002276</td>\n",
              "      <td>0.017224</td>\n",
              "      <td>0.132137</td>\n",
              "      <td>0.135835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_victor_20</th>\n",
              "      <td>0.000803</td>\n",
              "      <td>0.017623</td>\n",
              "      <td>0.045593</td>\n",
              "      <td>0.181569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prediction_target_teager2b_20</th>\n",
              "      <td>0.001069</td>\n",
              "      <td>0.015360</td>\n",
              "      <td>0.069606</td>\n",
              "      <td>0.142287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_victor</th>\n",
              "      <td>0.001530</td>\n",
              "      <td>0.017576</td>\n",
              "      <td>0.087045</td>\n",
              "      <td>0.162722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ensemble_cyrus_teager</th>\n",
              "      <td>0.001762</td>\n",
              "      <td>0.016228</td>\n",
              "      <td>0.108594</td>\n",
              "      <td>0.140513</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29f7286c-8964-4335-b3cd-7eab7651951a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-29f7286c-8964-4335-b3cd-7eab7651951a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-29f7286c-8964-4335-b3cd-7eab7651951a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-6e8ea0d7-eea4-41bb-8951-c4c96167ac55\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e8ea0d7-eea4-41bb-8951-c4c96167ac55')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-6e8ea0d7-eea4-41bb-8951-c4c96167ac55 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_e45f80ae-0cf6-4105-ae67-98b002b52c2e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e45f80ae-0cf6-4105-ae67-98b002b52c2e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  mean      std   sharpe  max_drawdown\n",
              "prediction_target_cyrusd_20   0.002276 0.017224 0.132137      0.135835\n",
              "prediction_target_victor_20   0.000803 0.017623 0.045593      0.181569\n",
              "prediction_target_teager2b_20 0.001069 0.015360 0.069606      0.142287\n",
              "ensemble_cyrus_victor         0.001530 0.017576 0.087045      0.162722\n",
              "ensemble_cyrus_teager         0.001762 0.016228 0.108594      0.140513"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "validation[\"numerai_benchmark\"] = (\n",
        "    benchmark_models\n",
        "    .groupby(\"era\")\n",
        "    .apply(lambda x: x.mean(axis=1))\n",
        "    .reset_index()\n",
        "    .set_index(\"id\")[0]\n",
        ")\n",
        "\n",
        "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, \"numerai_benchmark\")\n",
        "# plot the cumsum mmc performance\n",
        "cumsum_mmc.plot(\n",
        "  title=\"Cumulative BMC of Neutralized Predictions\",\n",
        "  figsize=(10, 6),\n",
        "  xticks=[]\n",
        ")\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%f' % x)\n",
        "summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-FSdGrhqWkD"
      },
      "source": [
        "Looking at the results above, none of these models seem very additive to models that Numerai can already create. It will take some research and experimentation to find something additive to Numerai's benchmarks.\n",
        "\n",
        "Ensembling models trained on different targets can be a very fruitful avenue of research. However, it is completely up to you whether or not to create an ensemble - there are many great performing models that don't make use of the auxilliary targets at all.\n",
        "\n",
        "If you are interested in learning more about targets, we highly encourage you to read up on these forum posts\n",
        "- https://forum.numer.ai/t/how-to-ensemble-models/4034\n",
        "- https://forum.numer.ai/t/target-jerome-is-dominating-and-thats-weird/6513"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihVoBcnhxnNq"
      },
      "source": [
        "## 4. Model Upload\n",
        "To wrap up this notebook, let's pickle and upload our ensemble.\n",
        "\n",
        "As usual, we will be wrapping our submission pipeline into a function. Since we already have our favorite targets and trained models in memory, we can simply reference them in our function.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "WWdOTGy4xnNq"
      },
      "outputs": [],
      "source": [
        "# we now give you access to the live_benchmark_models if you want to use them in your ensemble\n",
        "def predict_ensemble(\n",
        "    live_features: pd.DataFrame,\n",
        "    _live_benchmark_models: pd.DataFrame\n",
        ") -> pd.DataFrame:\n",
        "    favorite_targets = [\n",
        "        'target_cyrusd_20',\n",
        "        'target_teager2b_20'\n",
        "    ]\n",
        "    # generate predictions from each model\n",
        "    predictions = pd.DataFrame(index=live_features.index)\n",
        "    for target in favorite_targets:\n",
        "        predictions[target] = models[target].predict(live_features[feature_cols])\n",
        "    # ensemble predictions\n",
        "    ensemble = predictions.rank(pct=True).mean(axis=1)\n",
        "    # format submission\n",
        "    submission = ensemble.rank(pct=True, method=\"first\")\n",
        "    return submission.to_frame(\"prediction\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "kPq_ATf0xnNr",
        "outputId": "53bef369-0a53-4c1e-fd62-d9d4d6ec2c53"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "v5.1/live.parquet: 8.27MB [00:00, 21.9MB/s]                            \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"predict_ensemble(live_features, benchmark_models)\",\n  \"rows\": 6723,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          \"nd9351550a167617\",\n          \"n83f3a823c3d3ce0\",\n          \"n998459b1b948928\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28869660301670663,\n        \"min\": 0.00014874312063067083,\n        \"max\": 1.0,\n        \"num_unique_values\": 6723,\n        \"samples\": [\n          0.9815558530417968,\n          0.6483712628290942,\n          0.22772571768555705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-0af64474-90bd-46e0-8abd-2ec67b404b54\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n001ba451c4cf24f</th>\n",
              "      <td>0.851406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n00208b1df989b47</th>\n",
              "      <td>0.450840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002115a1e41ac5c</th>\n",
              "      <td>0.088353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n0021e1e026d7e47</th>\n",
              "      <td>0.433289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n002ddf4912dda8d</th>\n",
              "      <td>0.853191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffcf4d74ac07190</th>\n",
              "      <td>0.626804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffd30a4ec0c8662</th>\n",
              "      <td>0.227874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nffe131b7e72bc81</th>\n",
              "      <td>0.091031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nfff66b587bd248f</th>\n",
              "      <td>0.634836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nfff8eb83b5e7585</th>\n",
              "      <td>0.705191</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6723 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0af64474-90bd-46e0-8abd-2ec67b404b54')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0af64474-90bd-46e0-8abd-2ec67b404b54 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0af64474-90bd-46e0-8abd-2ec67b404b54');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-eb82d4d8-fea3-46da-bcfa-beb5fbc871f4\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eb82d4d8-fea3-46da-bcfa-beb5fbc871f4')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-eb82d4d8-fea3-46da-bcfa-beb5fbc871f4 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                  prediction\n",
              "id                          \n",
              "n001ba451c4cf24f    0.851406\n",
              "n00208b1df989b47    0.450840\n",
              "n002115a1e41ac5c    0.088353\n",
              "n0021e1e026d7e47    0.433289\n",
              "n002ddf4912dda8d    0.853191\n",
              "...                      ...\n",
              "nffcf4d74ac07190    0.626804\n",
              "nffd30a4ec0c8662    0.227874\n",
              "nffe131b7e72bc81    0.091031\n",
              "nfff66b587bd248f    0.634836\n",
              "nfff8eb83b5e7585    0.705191\n",
              "\n",
              "[6723 rows x 1 columns]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Quick test\n",
        "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n",
        "live_features = pd.read_parquet(f\"{DATA_VERSION}/live.parquet\", columns=feature_cols)\n",
        "predict_ensemble(live_features, benchmark_models)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "5VTrZ1Q6xnNr"
      },
      "outputs": [],
      "source": [
        "# Use the cloudpickle library to serialize your function and its dependencies\n",
        "import cloudpickle\n",
        "p = cloudpickle.dumps(predict_ensemble)\n",
        "with open(\"target_ensemble.pkl\", \"wb\") as f:\n",
        "    f.write(p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "RiJjBD-zxnNr",
        "outputId": "dd372d69-e8f7-4fec-8211-2078a3e18c6d"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_294d3996-c53b-463c-93e4-3171696b7157\", \"target_ensemble.pkl\", 13246614)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Download file if running in Google Colab\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download('target_ensemble.pkl')\n",
        "except:\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxO2YzmIxnNr"
      },
      "source": [
        "That's it! Now head back to [numer.ai](numer.ai) to upload your model!"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "gpuType": "V28",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.14"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
